{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dafc6964-9cda-401d-9adb-82132d2a71cd",
   "metadata": {},
   "source": [
    "# 实验3 回归模型的泛化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0bc47b-39be-46cc-a6d3-fa10e627bf08",
   "metadata": {},
   "source": [
    "### 步骤1 数据集准备"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f11d685-dc05-4251-8922-739fc622cd0c",
   "metadata": {},
   "source": [
    "#### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "be039381-c58a-41e3-b65c-81b78a4613f5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>396.90000</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>396.90000</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>392.83000</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>394.63000</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>396.90000</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1      2    3      4      5     6       7    8      9     10\n",
       "0    0.00632  18.00   2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3\n",
       "1  396.90000   4.98  24.00  NaN    NaN    NaN   NaN     NaN  NaN    NaN   NaN\n",
       "2    0.02731   0.00   7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8\n",
       "3  396.90000   9.14  21.60  NaN    NaN    NaN   NaN     NaN  NaN    NaN   NaN\n",
       "4    0.02729   0.00   7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8\n",
       "5  392.83000   4.03  34.70  NaN    NaN    NaN   NaN     NaN  NaN    NaN   NaN\n",
       "6    0.03237   0.00   2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7\n",
       "7  394.63000   2.94  33.40  NaN    NaN    NaN   NaN     NaN  NaN    NaN   NaN\n",
       "8    0.06905   0.00   2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7\n",
       "9  396.90000   5.33  36.20  NaN    NaN    NaN   NaN     NaN  NaN    NaN   NaN"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step :lood data\n",
    "import pandas as pd # To load house price data\n",
    "import numpy as np # To manipulate data\n",
    "data_url = 'D:\\BaiduNetdiskDownload\\houseprice.csv' # Set url of the dataset\n",
    "raw_df = pd.read_csv(data_url, sep=',', skiprows=1, header=None) # Load data\n",
    "raw_df.head(10) # Display the raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c43da4-c897-4a73-b66b-57ca7246e591",
   "metadata": {},
   "source": [
    "#### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "106b908d-0363-4715-acdf-2be8990fa0d6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (506, 12), Target shape: (506,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, 1:2]]) # Select features\n",
    "target = raw_df.values[1::2, 2] # Select target\n",
    "\n",
    "fts_names = [\n",
    "    '犯罪率（%）',\n",
    "    '大住宅用地占比（%）',\n",
    "    '非零售商业用地占比（%）',\n",
    "    '景观房',\n",
    "    '氮氧化物浓度（ppm）',\n",
    "    '平均房间数',\n",
    "    '老旧房屋占比（%）',\n",
    "    '离就业中心的加权距离',\n",
    "    '辐射路可达性指标',\n",
    "    '每万元房产税',\n",
    "    '学生-教师比',\n",
    "    '低层次人口占比（%）'] # Feature names\n",
    "\n",
    "print(f'Data shape: {data.shape}, Target shape: {target.shape}') # Display the shape of data and target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99aeb50-7231-4677-ae3c-3625f200581e",
   "metadata": {},
   "source": [
    "#### 安装字体才能在figure中显式中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1c3567f2-412e-4986-96ac-8737eb02a0dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import font_manager\n",
    "\n",
    "font_manager.fontManager.addfont('D:\\BaiduNetdiskDownload\\simhei.ttf') # Add the font\n",
    "matplotlib.rc('font', family='SimHei') # Set the font"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40bbb18-9a44-4865-a3e0-a9271bb3c0b7",
   "metadata": {},
   "source": [
    "### 步骤2 使用scikit-learn的train_test_split函数将数据集分离为训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "dfd44509-29e8-4091-b402-4bda60a9af79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AQidXgtuwYcMUHBxs+75z5059+umn6cotW7ZMd911lz755JPcaAYAAEChkq3gdv36dTVr1kzz5s3L8Ly/v7/8/f1t3xcuXKhhw4alK3fw4EElJibKarU6dF+r1ao//vhDf/75Z3aaDQAA4NayFdzMZrP279+vmJgYSdLp06c1ceJE20K63t7e8vL630oj/wxyknTjxg0dOnRIJpNJnTp1uu09P/vsM5UvX141a9bUHXfcodq1a2vnzp2287t375bJZEr3KVmyZHZ+IgAAQIGTrXXcvL29b138/8PZX3/9penTp6tjx46qXr26PDw85Onp+b+beHnZrkmzYcMGpaSkKDQ0VJUqVcryftu3b1dYWJhGjhypgQMH6sKFCxo1apR69Oih33//XaVKldLevXvl4+Ojjz76yO5aHx+f7PxEAACAAsep4JaamipPT0+ZTCZJsoUzX19fGYYhX1/fDK/z9PS0C3LSreFTk8mkUaNG3fa+48ePV5cuXTR37ly7tjzyyCPauXOnunfvrt27d6tx48YKCwtz5icBAAC4DYeHSg3DUJs2bfTNN9+kO2c2m2UymTLdNSFt2DLNDz/8oG3btql58+bq0aNHlve1Wq2aNm2a5syZk+6e0v/C4549e9S2bVtHfw4AAIDbcTi4ffbZZ9qzZ4+6deumsWPHpjtvGIZdOMtMQkKChg0bJh8fHy1YsOD2DfTwUKdOnVSrVi3bsaSkJM2ePVslS5ZUq1atdP78eUVERCg8PFxVqlSRr6+v6tatq9mzZys1NdXRnwgAAFCgOTxU2q9fPwUEBOjNN9/UnDlzHAppGRk4cKB++eUXvfvuu6pfv75T116/fl3Tpk3TmjVrFBUVpZUrV6pkyZLasmWLJCkmJkbDhw9XiRIltGrVKo0dO1YXL17UW2+9lWmdFotFFovF9t2R9ecAAADyg1OzSrt166bdu3fr888/l7NbnKampqp///5auXKlhg8frnHjxjl1vXSrp+3QoUM6d+6czGazEhISJElVq1bVtGnTtH//fr388ssaMWKEtm7dqk6dOmnevHmKjY3NtM4ZM2YoMDDQ9gkJCXG6XQAAAHkh25vMe3h4qHPnzmrTpo0uXryoefPmaeTIkSpfvrw2btyoM2fOaO3atdq5c6d2796t1atXy2Qy6YUXXtDMmTOz3WMnSVeuXFG/fv20Y8cOHTp0SHfffXeG5VauXKknnnhCGzduVNeuXTMsk1GPW0hICJvMAwCAPOPoJvPZWg4kzbfffqvNmzfbvv99Qd7y5csrPDxcL774oi2kNWnSRBMnTsxRaJOkoKAgzZ8/X7Vq1dLq1aszDW7FihWTJJ0/fz7Tusxmc6aTKgAAAAqSHG15NXjwYG3ZskXvv/++JGnBggXasmWLHn30UUlSuXLlNGTIENWuXVuSdODAAdWvX18HDx50+B5Xr17Va6+9pp9//tnueFBQkCQpOTlZ48eP1zvvvJPu2kOHDtnaAQAA4O6cDm6XLl3SsWPHJEk1a9ZUhw4d1KJFC0lS8+bN1aFDB9t7Yv369dMHH3ygLl26qGLFipo7d66io6PVrl077dmzx6H7BQQEaMGCBXrhhRfs3qtbvXq1JKl169aKiIjQzJkzdfnyZdv5iIgIzZkzRyVKlFD79u2d/ZkAAAAFjlNDpT/++KMef/xx1apVy+nhTg8PD40cOVKNGzdWly5d9Oijj+ro0aO2nrNMG+jlpZkzZ2rw4MFq3769nnjiCZ07d06zZs3Sww8/rA4dOqh8+fJau3at7r33XnXv3l0Wi0WrVq1STEyMFi5cyLtqAACgUHC4x+3o0aNq3769oqKi1KpVK6dnlaZp1aqV3n//fV28eFEvvfSSQ9cMGjRIK1as0I0bN/TSSy9p5cqVevXVV7VixQp5eHiofv36+v7771WjRg0tWrRIX375pVq0aKHt27dr0KBB2WonAABAQeNwj1uDBg3Uu3dvDRs2TC1bttSMGTNs59IWuU1JSXGorrCwMH3yySdaunSpXn31VdWsWfO21zz++ON6/PHHMz3frFkzbdu2zaH7AwAAuCOn3nFbsmSJWrZsafue1utmsVhkMpnsltX4u+TkZCUlJdkde+mll5SSkqJp06Y522YAAIAiKVuzStN62NL+abFYZBhGpsEtKSkpXXB74IEHVLVqVa1YsUI3b97MTjMAAACKlGwFt8TEREmy7VxQv359rV+/3raFldVqtdsjNKPgJkmdOnWSxWLJcON6AAAA2MtWcDMMQ4899pjq1KkjSSpTpoweeughlSlTRtKtoJacnGwrb7FYMgxurVu3lmEY2r59e3aaAQAAUKRka+eE4sWLa+XKlZmeNwxDXl7/q3rWrFmaPHlyunINGzbUW2+9pRdeeCE7zQAAAChSsr1XaWHl6F5hAAAAruJo/sjRllcAAADIOwQ3AAAAN+Hy4JaYmKi5c+dq2bJlrq4aAACgSMuV4DZmzBj95z//cXXVAAAARZrLg5vZbJYkFStWzNVVAwAAFGlOB7cTJ05keT4tuP19ORAAAADknMPB7ebNm3rzzTfVvHlzxcXFZV6hx60qPT09c946AAAA2Dgc3KKjozVp0iQlJCQoMjIyN9sEAACADDgc3EJCQvTggw9Kkv74449caxAAAAAy5tQ7br169ZJhGPrll19yqz0AAADIhFPBrXPnzpKkn3/+OVcaAwAAgMw5FdyCg4NVq1YtghsAAEA+cHrNjkaNGmnTpk369NNPldX+9BcuXNCSJUsyPGe1WmWxWGSxWJSYmKiXXnrJ2WYAAAAUOSYjq/SVgTfeeEOTJk2SyWTKtIxhGFme/2e51NRUZ5qQq+Li4hQYGKjY2FgFBATkd3MAAEAR4Gj+cLrHrUaNGpKk0NBQ3XXXXRmWmT9/vipXrqzu3btneN4wDKWmpiopKUkJCQnONgEAAKBIcjq43XHHHZKk+++/X6+++mqGZebPn686derov//9b44aBwAAgP9xesurypUrS5LOnj3r6rYAAAAgC04HtwoVKshkMikiIiI32gMAAIBMOB3cvL29VapUKf3111+50R4AAABkwungJklly5bVuXPnXN0WAAAAZCFbwS0oKEhxcXG6ceOGq9sDAACATGQruJUsWVKS9Oeff2ZapiCtzQYAAFAYZCu4NWjQQOvXr1fdunXTnUtJSZEk1mcDAABwMafXcZOk6dOnZ3ouMTHR7p8AAABwjWwFt6x4eHho/PjxCg4OdnXVAAAARZrTe5UWduxVCgAA8pqj+SNb77gBAAAg77lVcLNarfrjjz+ynM0KAABQWDkd3GJiYlxy46SkJKfKf/bZZypfvrxq1qypO+64Q7Vr19bOnTvtyqxbt05NmjSRv7+/6tevr61bt7qkrQAAAAWBU8Ht1VdfVePGjRUdHZ2jm1qtVj388MMaMWKEQ+W3b9+usLAw9e7dWwcOHNCGDRuUkpKiHj162ILk8uXL1bNnT6WkpGjGjBmqU6eOunbtqqNHj+aorQAAAAWFU5MT1q5dq169eumee+7Rli1bVKJEiWzddPDgwVq8eLGGDRum995777blmzVrpqCgIH399de2Y1999ZUeeeQRrVu3Th06dFC1atVUvHhxHT58WAEBATIMQ6GhofLw8NC2bdscbhuTEwAAQF7LlckJPXr00AcffKD9+/erQ4cOunTpklONSk5OVlhYmBYvXqzOnTtr7ty5t73GarVq2rRpmjNnjt1xs9ksSfL09NSOHTsUFRWlkSNH2n6syWTSsGHD9P333+vKlStOtRMAAKAgcvodt0GDBmnp0qU6fvy4GjVqpNWrVzt03U8//aR7771Xn332mR566CGtWbNGXl63X0bOw8NDnTp1Uq1atWzHkpKSNHv2bJUsWVKtWrXSsWPHJEmhoaF21zZt2lRWq1WHDx/OtH6LxaK4uDi7DwAAQEGUrVmlvXv31q5du1S6dGk9/vjjat68uT766CNFRkbalbt06ZJWrFihhx9+WC1atNAvv/yiyZMn66uvvpKvr6/T971+/brGjx+vevXqac+ePVq5cqVKlixpe8+tevXqduXTFgE+e/ZspnXOmDFDgYGBtk9ISIjT7QIAAMgL2d45oUmTJjp8+LDmz5+vf//73xo6dKhMJpP8/PxUvHhx3bhxQwkJCTIMQz4+PgoLC9OECRNUu3btbDc2KSlJhw4d0rlz51SiRAnbfqhWq1WSVLx4cbvy/v7+kqRr165lWucrr7yicePG2b7HxcUR3gAAQIGUoy2vvL29NWbMGI0ePVo//PCDwsPDdfbsWd28eVNms1khISFq1KiROnbsmC5UZUeZMmX07bff6sqVK+rXr5969eqlQ4cOyd/fXyaTSf+cZ5H2Pat9U81ms+19OQAAgILMJXuVmkwmtWvXTu3atXNFdbcVFBSk+fPnq1atWlq9erUqVaokwzAUGRmpatWq2cpFRUVJErNDAQBAoVDgd064evWqXnvtNf388892x4OCgiTdmqnauHFjSUq3IO++ffskSRUqVMiDlgIAAOSuAh/cAgICtGDBAr3wwgt2Q6Fps1lbt26tpk2bqkqVKpo3b55SUlJsZRYuXChPT890s00BAADckUuGSnOTl5eXZs6cqcGDB6t9+/Z64okndO7cOc2aNUsPP/ywOnToIA8PD02aNEmDBw9Wnz59NGzYMK1atUobN27UgAEDbL1zAAAA7sypnRPy08qVK/XWW2/pl19+UcWKFfXUU0/pxRdflJ+fn63MO++8o9dff10Wi0WS9PDDD2vZsmVOvePGzgkAACCvOZo/3Ca4OerSpUvat2+fKlasqKZNmzp9PcENAADkNUfzR4EfKnVWcHCwunXrlt/NAAAAcLkCPzkBAAAAtxDcAAAA3ATBDQAAwE0Q3AAAANwEwQ0AAMBNENwAAADcBMENAADATRDcAAAA3ATBDQAAwE3kKLhZrVb99ttvunjxYrauP3LkSE5uDwAAUKTkKLjFx8frrrvu0qRJk5y+NiUlRb1799awYcOUnJyck2YAAAAUCTnaq7RYsWIyDEPHjh3TrFmzFBwcrDvuuEO1atVSuXLlsrx26tSp+vXXX5WYmKiEhAR5e3vnpCkAAACFXo6Cm4fHrQ67vXv3as+ePTKZTLZzpUqVUsuWLdWmTRuFhobqnnvusZ376KOPNH36dHl5eWnZsmUKCAjISTMAAACKhBwFtzQ9e/ZUr169dP78eZ07d04XLlzQiRMn9PXXX2vDhg0ymUxq3LixRo8erZSUFD377LOSpPnz56tly5auaAIAAECh55LgVqVKFfXu3Tvd8fj4eG3fvl2ffvqp1q9fr4EDB0qSfH19tXDhQvXp08cVtwcAACgSnJqc8P3332vRokWKjY11qHyxYsXUvn17NW/eXH5+fjIMQ4ZhKDg4WO3atctWgwEAAIoqp3rcFi5cqM8++0zPPfecWrVqpU6dOmVYLiIiQtu2bdPmzZu1fv16JSQkqGzZspo8ebI8PT31+uuv64EHHtDevXtVvHhxl/wQAACAws6p4HbnnXeqc+fOOnTokHbs2KEdO3ZIkj7++GNFR0fr/PnzOnr0qK5cuSLDMOTp6ak2bdqoX79+CgsLk9lsliQlJCTozTff1LBhw7R06VKX/ygAAIDCyGQYhpGdC48fP67Nmzfrm2++0Y4dO2S1Wm2zSjt27Kjnn39eLVu2VKlSpdJda7Va1bZtW+3evVtff/21OnfunLNf4UJxcXEKDAxUbGwss10BAECecDR/ZHsB3nr16qlZs2Z69NFHdeTIEc2YMUN33nmnDMPQ1q1btXDhwgxDW5oPPvhAnp6eGjNmjFJTU7PbDAAAgCLDqeB27tw5NW/eXFu2bJEkffXVVxo1apSKFy+u8ePHa+fOnXrxxRdVr149lSpVSlOmTNGGDRtksVhsdcTGxqp27do6duyYBg8erIiICJ04ccK1vwoAAKAQcuodt507d+rIkSPq0qWLGjdurObNm0uSzGaz4uLi1KVLF50+fVonT56Ur6+vmjdvrqlTp6pEiRIaMWKExo0bp08++USnT59WSkqKXnrpJfXo0UMNGjTIlR8HAABQmDgV3Hr37q2OHTtq3rx5+uabb1S2bFlJtyYb9OjRQwcPHtTs2bPttrsym80qW7asZsyYoblz58rPz0/BwcHq06ePPDw8VK1aNdf+IgAAgELK6XfcgoKCNHnyZLstrooVK6Y777xTK1eu1MiRI+3KlyxZUr/99ptWrlypMmXK6MqVK6pevbptuywAAAA4xqlZpc2aNZO3t7fMZrM8PDx09uxZnTlzRm3btpWXl5eSk5OVlJSk6dOnq02bNqpfv75iY2N1/vx5SdKSJUs0cOBAmUwmjR49WrNmzcq1H5ZdzCoFAAB5zdH84dRQ6cGDB2W1WtMd/+GHH/T3/Ldu3TpNmDBB165ds9t4Pjw8XOXLl9d9992nOXPmqHv37mrfvr0zTQAAACiynBqvPHv2rC5cuKArV64oNjZWEyZMsJ0bNGiQrl27pgsXLigxMVF79uzRpUuXdPXqVb377rtKTEzU999/r9DQUC1dulSVKlXS6NGjXf6DAAAACiunglvlypUVHBysZcuW6auvvpKX160Ou06dOmnx4sV69dVXFRwcrP/+97/6+uuvFRQUpKSkJI0fP17Vq1fXqVOndP/998vPz09Tp07V8ePHtWrVqlz5YQAAAIWNU8EtJSVF/fv315gxY/T+++8rJSVFJpNJH374ofr376/3339f8+bNk7e3t7p06aLixYsrKChIn3zyiW1P0o4dO0qS+vbtq1KlSmn27Nku/1EAAACFkVPBbePGjVq6dKkGDBigLVu2KDk5WZJksVj0wQcfqEmTJnrhhRd07Ngx2/HU1FSFhYXp559/1uLFi1W1alVJko+Pjx5//HH9+OOP+uWXX1z8swAAAAofp4LbI488olWrVmnRokXy8/PTzZs3ZRiGLBaLvL29tXjxYkmyDX8mJiYqISFBkuTp6an+/fvb1ffggw/qgQceyHJrLAAAANyS7U3mM/PBBx/omWeekSR9++23SklJUdeuXTMsmxbq/Pz8XNmEHGE5EAAAkNdyfZP5zKSFNunWpIXMQpt0K7A5GtrmzZun2rVry9vbWyVKlNCTTz6py5cv287v3r1bJpMp3adkyZLZ/i0AAAAFiVPruOWXmTNn6uWXX1aXLl00btw4/f7775o9e7ZOnTqln376SZ6entq7d698fHz00Ucf2V3r4+OTT60GAABwrRwFt7Nnz8owDKf3G71586ZatGihChUqaPPmzVmWjYqK0pQpUzR27Fi7nRbMZrPefPNN7d69W61bt9bu3bvVuHFjhYWFZeu3AAAAFHQ5Cm4tW7bUjRs3FBcX59R1vr6+On78uCwWy23L3rhxQxMmTEi3WG+jRo0k3Qp2krRnzx49+eSTTrUDAADAneToHbdixYrJ19fX+Zv+/w3mPT09b1u2WrVqmjBhgm0duDR79+6VJDVs2FDnz59XRESEwsPDVaVKFfn6+qpu3bqaPXu2UlNTnW4fAABAQZSj4Obl5WXbPUGSHnjgAc2dO1d//vlnjhuWlejoaC1atEihoaGqXr26wsPDJUkxMTEaPny4/v3vfys4OFhjx46125YrIxaLRXFxcXYfAACAgihHy4Hcddddio2N1fnz53XmzBnVqFHDtql8nTp11LFjR91///1q165durXaPDw8dOedd+rkyZNO37dfv35avny59u3bpyZNmmjfvn3asmWLxo4dK39/f0mS1WrVgw8+qPDwcJ07d06BgYEZ1jV58mRNmTIl3XGWAwEAAHklz5cDuXTpkho3biyz2SzDMPTLL79o3rx5euyxx1S2bFk1atRIo0eP1urVq23vpWXHwoUL9dlnn2nSpElq0qSJJKlZs2aaMGGCLbRJt4LhkCFDFB8fb+uRy8grr7yi2NhY2ycyMjLbbQMAAMhN2ZqcYLVa9c+OuhYtWujAgQOyWq06deqUDh8+rP379+vAgQPav3+/jh49qqNHj2revHnZbuzevXs1cuRIde3aVa+99tptyxcrVkySdP78+UzLmM1mmc3mbLcJAAAgrzgd3L7//ns9//zzGQ4vSrd6uurUqaM6derYZnlarVYdOXJE4eHh2rp1q3bs2OH0u2RnzpzRI488ourVq+vzzz+3DclK0vjx4xUUFKQXX3zR7ppDhw5JksqVK+fUvQAAAAoih99xu3DhgsaOHauVK1fKMAy1a9dOFy9etL3j5oyUlBT5+Pg4/I7b+fPn1aZNG12/fl179uxR9erV7c736dNHW7Zs0cmTJ20hLSIiQvfcc48SExP1119/Ofy+GlteAQCAvOZo/nC4x81isWjNmjXy8fHRqFGjNGbMGHXo0MHhBv3222/aunWr6tatq3bt2jl8nXRrMsLp06c1cuRI/fjjj/rxxx9t5xo0aKBXX31Va9eu1b333qvu3bvLYrFo1apViomJ0cKFCwlgAACgUHBqVukHH3yg0NBQ1apVS5L9rNJ/SkhI0Ndff62NGzdq69atOnfunCTpwQcf1IYNGxyeVXr16lWVKVMm0/OTJk3S5MmTtW/fPr388svau3evzGaz7rvvPr300ktOh0R63AAAQF5zeY+bZL+BfGY3Xb9+vVatWqXNmzcrMTHRNomhZMmSatu2rTp16uTMLVW6dOl0EyEy0qxZM23bts2pugEAANxJjra8MgxDVqvV9v2JJ57Qli1bZBiGPDw8dN999+mRRx5Rhw4d1LhxY7sJBQAAAHBOjoJbcnKyEhISbN/79u2rmJgY9evXT717977tbM4crP0LAABQ5OQouMXFxSkxMdH2vX///urfv/9tr0vbP/TvvXUAAADImlPBLSYmxm7rqoMHD2ar1+zmzZuSbs1UBQAAgGOc2vJqzJgxat26tVavXq2UlBSFhISoSpUqTt+0RIkSslqtOnv2rNPXAgAAFFUOLwfy559/qk6dOkpOTpYklSpVSj169FC7du3k6enp9I1TU1Nt78iNGDHC6etzC8uBAACAvOZo/nA4uEVHR+vLL7/Uhg0btG3bNiUkJOR4lqhhGDKZTLZ33goCghsAAMhrLg9uf5eQkKAvvvhC7733ng4cOGA7HhQUpEGDBsnPzy/L661Wq5KSknTz5k1dvXpVn376qbNNyDUENwAAkNdyNbj93b59+zRz5kytXbtWklSpUiW99dZb6tu3b06qzTcENwAAkNcczR9OTU7ISLNmzbRq1SodOHBAbdu21V9//aWnnnpK7du3119//ZXT6gEAAPD/5Ti4pWnUqJG2b9+uDz/8UCVKlNCjjz6qSpUquap6AACAIi/HQ6UZiY+PV7FixVxdbZ5gqBQAAOS1PBsqzYi7hjYAAICCLFeCGwAAAFyP4AYAAOAmcrTJvCR5e3vLbDbL29tbXl5edovyWq1WJScnKykpSYsWLVKfPn1yejsAAIAiK8fBLTU11bZpfFZyYQ4EAABAkeKSodJBgwbpzJkzdp/Tp0/bnevRo4crbgUAAFBk5bjHTZICAgJUtWpVp88BAADAcUxOAAAAcBMENwAAADdBcAMAAHATBDcAAAA34fDkhPPnz2vKlCkym83y8fGRp6enPDxu5b7w8HC9+uqrGV6X0TnDMGzru1ksFi1YsCAHPwEAAKBocHiT+RMnTqh+/fp2C+zmZG02k8kkwzBkMpmUmpqa7XpcjU3mAQBAXnM0fzjc41aqVCkNGjTItkOCt7e3vL299c477+jee+/V/fffb1feMAy9/fbbGZ5LTU1VUlKSbt68qejoaCd/GgAAQNHkcI9bZjw8PDRmzBjNmjXLqXMFFT1uAAAgrzmaP5icAAAA4CYIbgAAAG7CJVteASi8Uq2G9p25qsvXE1WuhK+aVSstTw/T7S8EALgcwQ1ApjYdv6Ap60/qQmyi7ViFQF9N6na3utSrkI8tA4CiySXBLS4uThEREVmeK1u2rPz8/FxxOwB5YNPxC3pu6UH9c/bSxdhEPbf0oN4Pa0J4A4A85pJZpX9f2y0zn376qfr27ZuTW+UJZpUCt4ZHW8/cZtfT9ncmSeUDfbVr/P0MmwKAC7h8Hbes/H03BZPJZFtc12q1KiUlRRaLxRW3AZBH9p25mmlokyRD0oXYRO07c1X31SiTdw0DgCIux7NKrVarbt68qWvXrik6OlpXrlxRVFSUrly5oqtXryouLk4WiyXHvW3z5s1T7dq15e3trRIlSujJJ5/U5cuX7cqsW7dOTZo0kb+/v+rXr6+tW7fm6J5AUXX5euahLTvl3EWq1dDuP6K17vA57f4jWqnWHA1IAIDLucXkhJkzZ+rll19Wly5dNG7cOP3++++aPXu2Tp06pZ9++kmenp5avny5+vTpo3r16mnGjBnauXOnunbtqv3796tBgwb5/RMAt1KuhK9Ly7kDJmIAcAc5fsctt0VFRalq1aoaNmyY3Q4MEyZM0JtvvqmdO3eqcePGqlatmooXL67Dhw8rICBAhmEoNDRUHh4e2rZtm8P34x034H/vuF2MTUw3OUEqfO+4ZTYRI+2XMREDQG4rNDsn3LhxQxMmTNDUqVPtjjdq1EjSrWC3Y8cORUVFaeTIkbYfazKZNGzYMH3//fe6cuVKXjcbcGueHiZN6na3pP+FlzRp3yd1u7tQhLZUq6Ep609mGFDTjk1Zf5JhUwAFgsuC27JlyzRt2jRXVWdTrVo1TZgwQcWLF7c7vnfvXklSw4YNdezYMUlSaGioXZmmTZvKarXq8OHDmdZvsVgUFxdn9wEgdalXQe+HNVH5QPvh0PKBvoWqB8qZiRgAkN9c8o5bcnKyJk6cqLNnz6pYsWIaO3asK6rNVHR0tBYtWqTQ0FBVr15dMTExkqTq1avblQsODpYknT17NtO6ZsyYoSlTpuRaWwF31qVeBT1wd/lCvXNCUZ2IAcA9uSS4zZo1S2fOnFGFChU0aNAgV1SZpeeff15xcXF69913Jd2a2SopXa+cv7+/JOnatWuZ1vXKK69o3Lhxtu9xcXEKCQlxcYsB9+XpYSrUS34UxYkYANxXjoPbnj179Prrr8vLy0uLFy9WYGCgK9qVqYULF+qzzz7T1KlT1aRJE0m3Alra2nF/l/Y9MTHz/1I2m80ym82512AABVqzaqVVIdD3thMxmlUrnddNA4B0chTcIiMj9eSTTyolJUXvvfeemjZtqkaNGsnPz8+2IG9mUlJSlJiYKIvFoiNHjjh0v71792rkyJHq2rWrXnvtNdvxSpUqyTAMRUZGqlq1arbjUVFRksTsUACZSpuI8dzSgzJJduGtsE3EAOD+sh3cTp8+rQ4dOigyMlIzZszQsGHDdOnSJR09etSpehzZLkuSzpw5o0ceeUTVq1fX559/bndd48aNJUk7d+60C2779u2TJFWoUDheogYKulSr4Zbvw6VNxPjnOm7lWccNQAGTrXXcNm3apEGDBikqKkoLFiywvdeWmpqqkydPys/PT2azWR4emU9aTUpKksVikcViUcOGDbO83/nz59WmTRtdv35de/bsSTcJwWq1qlq1agoODtaPP/4oL69befThhx/Wpk2bdPHiRQUFBTn021jHDciewrCArbsGTwDuz9H84VRwGzp0qEwmkz766CNVqVJFS5cuVatWrVzS4KyEhoZqx44dGjlypJo3b253rkGDBmrQoIEWLVqkwYMHq1evXho2bJhWrVql999/XwMGDNDHH3/s8L0IboDzWMAWAHLG5cHt22+/VZcuXWQymRQUFKRDhw6pYsWKLmtwZq5evaoyZTKf0TZp0iRNnjxZkvTOO+/o9ddft21q//DDD2vZsmVOBTCCG+CctF0WMlsLrbDtsgAAucHlOyd4e3vrwQcflMlkUlRUlFq1aqWNGze6pLFZKV26tAzDyPSTFtok6cUXX9Sff/6pr776Svv379f69esJX0AuYwFbAMg7Dk9OCA0NVWhoqH777TdNnjxZy5cvV/fu3TV8+HD95z//sb1XNnnyZJUuXVoVK1ZU+fLlFRwcrLJly6pkyZK59RvsBAcHq1u3bnlyLwAsYAsAeSnbm8zv3btXTz/9tH799Ve1aNFCGzZsUKlSpeTr66vk5OR05YsXL66GDRvqoYce0tChQ1W6dMFcE4mhUjjDVS+zu/NL8bv/iFafD/fcttznQ1sU6oV8ASAncmVywj9ZLBYNHDhQy5cvV4MGDbRhwwatWrVKnp6eSklJUUJCgmJjYxUdHa2jR4/qyJEjSkpKkr+/v2bOnKkRI0Zk99a5huAGR7lqFqW7z8ZMe8ftdgvY8o4bAGQuT4JbmmeffVYffvih6tevrx9//FHFihXLsNzly5f173//W//5z3+Umpqqnj176osvvrANsxYEBDc4wlWzKAvLbMy03yFlvICtu/wOAMgvLp+ckJX3339fDz/8sI4dO6Z+/fplWq5cuXKaOXOmtm7dqjJlymjNmjV64oknXNEEIM+kWg1NWX8yw96ltGNT1p9UqjXr/yZyVT0FQdoCtuUD7ffzLB/oS2gDABdyuKvr5s2bWr16tfr27ZtuYV0PDw8tXbpUDRs21MWLFxUXF5dlWmzbtq02btyo9u3bq3PnztlvPZAPnJlFmdU7Xa6qp6DoUq+CHri7vNu+qwcA7sDhHrdvv/1WAwYMUO3atTVv3jzFx8fbnQ8ICNDatWv17bffOjTEeO+99+rAgQN69tlnnW81kI9cNYuyMM7G9PQw6b4aZfRIo0q6r0YZQhsAuJjDPW6//PKL/P39dfr0aY0ePVqvvfaa+vXrp0qVKjl9U8MwlJqaquTkZCUkJOjdd991ug4gv5Qr4Xv7Qg6Uc1U9AICiw6nJCUlJSdq+fbs++OADffXVV0pNTbXb7D078xxMJpNSU1Odvi63MDkBt+OqWZTMxgQApHE0fzg1ndPHx0edO3dW586dFRkZqblz5+q9995TQkKCPD099fTTT+uxxx67bT1JSUlKTExUcnJygQptgCM8PUya1O1uPbf0oEzKeBblpG533zZsuaoeAEDRkePlQCIjIzV+/HgtX75cknT//ffrww8/1B133OGK9uU5etzgKNZxAwC4Sp6u4yZJO3bsUJ8+fXTp0iVVrlxZP//8c6bruRVkBDc4Im2ng4uxCboan6TSxc0qH1A0d04AAORcrgyVZqV9+/Y6ePCgnnzySb322mtuGdoAR2TVQ5bdsJU2GxMAgKy4rMetsKDHDVkpLDsdAAAKljzdOeGfbt68qYkTJ2revHm5UT2QLwrTTgcoWFKthnb/Ea11h89p9x/RPEMAMpUrm4SmpqZq2rRpaty4sUaOHJkbtwDyXGHb6QAFA5NTADgjW8Ft/vz52rBhg/z8/GQ2m5WamqqbN29q8uTJuueee+Tn5ydJtn9K0vTp01WjRg3VrFlTtWvXZhgSbqcw7nSA/JXZ0PvF2EQ9t/QgQ+8A0slWcPv999+1efNmu2Mmk0nPP//8rUq9vOz+KUmvv/663WK9lStXVosWLTR8+HC1a9cuO80A8hQ7HcCVbjf0btKtofcH7i7PDGMANtl+x81kMunChQu6cOGCtmzZkuGuCf/cjL5kyZJ69NFHVa9ePV26dEkrV65U586dFRERkd1mAHmmWbXSqhDoq8z+L9SkW0NczaqVzstmwU05M/QOAGlyNDkhODhYwcHBCgoKcrj8ypUrdeTIEV24cEHt2rVTcnKydu3alZNmAHkibacDSenCGzsdwFkMvQPIjlyZVeqIUqVKqW/fvjIMQ6dOncqvZgBO6VKvgt4Pa6LygfbDoeUDfXkfCU5xx6F3Zr8C+S9XZpU6ymw2S7q1bRbgLrrUq6AH7i7PTgfIkbSh94uxiRm+52bSrf8gKChD78x+BQoGl/e4paSk6Msvv3SorMViUatWrdS5c2dXNwPIVWk7HTzSqJLuq1GG0AanudPQe9rs13++k5c2+3XT8Qv51DKg6HE6uFksFkVFRWV4bu3atapataqefPJJuxmkmenfv7927typxx9/3NlmAIDbc4ehdxaeBgoWh4dK9+/fr3feeUcbN25UQkJChmXKlSunCxcuqF69ejp+/Pht60wbKgWAoqqgD72z8DRQsDgc3M6cOaOVK1dKkjw9PWW1WtOVad68udauXavu3bvLw8NDv//+uyZOnKiyZctKUobXAEBRlzb0XhAx+xUoWBweKn344Yc1dOhQ7dq1SyNGjMiwjKenp7p37277fu7cOU2bNk1jxoyRJJ06dUoNGzbUggULMlz3DQBQsLjj7FegMHM4uPn5+WnBggVq2bKlQ++vSVKdOnU0f/58vfHGG+rfv79CQkJ07Ngx224Jf/75Z7YbDgDIfSw8DRQsubocSPny5fXcc8/ZHfvuu+/0r3/9S7t27VKbNm20Y8cOVa9ePTebAQDIprTZr88tPSiTZDdJoaDNfgWKgjxfgLdDhw7au3ev+vXrp7/++kuhoaG6cuVKXjcDAOAgd5j9ChQV+bIAr4+Pj5YsWaK4uDitX79egwcP1rp16/KjKQAABxT02a9AUeHS4Obou29pZRcvXqw6derIMAxZLBaWBwGAAqwgz34FiopsBzfDMBQQECDp1jIfJpNJPXr0kKenp6RbwWzfvn1q1aqVKleurOrVq6t169Zq3bq1AgMDJUmlS5fWwoUL1bVrV3l7e7vg5wAAABRe2QpuJpNJ3t7eslqt8vHxkYeHh/z9/eXh4SHDMGS1WmW1WpWQkKDdu3fbrnv77bdlMpnUqlUrPfXUU3r88cf1yCOPOHXvESNG6MSJE9qxY4fd8d27d6tly5bpygcGBuratWvZ+ZkAAAAFSraC26xZszRr1qzbljMMQzExMTp//rxOnDihQ4cOacOGDdq5c6d27dqlMWPGaOTIkXrppZdUuvTtp5K//fbbeu+999SuXbt05/bu3SsfHx999NFHdsd9fHwc/2EAAAAFmMnIh5VwDxw4oOnTp2vt2rXy8PDQihUr9Oijj2ZaPjk5WaNGjdLHH3+sEiVKqG7duul63J588kn9+eef2rNnT47aFhcXp8DAQMXGxtqGggEAAHKTo/kjz5cDkaSmTZtq9erV2rp1q8aMGZNlaJOkH3/8URs3btTWrVtVt27dDMvs2bNHbdu2zY3mAgAAFAj5EtzS3H///Xr33XdvW+7OO+/U8ePH1bp16wzPnz9/XhEREQoPD1eVKlXk6+urunXravbs2UpNTXV1swEAAPJFvqzj5qzg4OAsz4eHh0uSYmJiNHz4cJUoUUKrVq3S2LFjdfHiRb311luZXmuxWGSxWGzf4+LiXNNoAAAAF8vXHjdXqVq1qqZNm6b9+/fr5Zdf1ogRI7R161Z16tRJ8+bNU2xsbKbXzpgxQ4GBgbZPSEhIHrYcAADAcYUiuDVr1kwTJkyQv7+/7ZiHh4eGDBmi+Ph4W49cRl555RXFxsbaPpGRkXnRZAAAAKe5xVBpdhUrVkzSrXfgMmM2m9mxAQAAuIVC0eM2fvx4vfPOO+mOHzp0SJJUrly5vG4SAACAyxWK4BYREaGZM2fq8uXLdsfmzJmjEiVKqH379vnXOAAAABcpFEOlr776qtauXat7771X3bt3l8Vi0apVqxQTE6OFCxeykC4AACgUCkWPW/369fX999+rRo0aWrRokb788ku1aNFC27dv16BBg/K7eQAAAC6RL1teFWRseQUAAPJagd7yCgAAAM4juAEAALgJghsAAICbILgBAAC4CYIbAACAmyC4AQAAuAmCGwAAgJsguAEAALgJghsAAICbILgBAAC4CYIbAACAmyC4AQAAuAmCGwAAgJsguAEAALgJghsAAICbILgBAAC4CYIbAACAmyC4AQAAuAmCGwAAgJsguAEAALgJghsAAICbILgBAAC4CYIbAACAmyC4AQAAuAmCGwAAgJsguAEAALgJghsAAICbILgBAAC4CYIbAACAmyC4AQAAuAmCGwAAgJsguAEAALgJr/xuQGGTajW078xVXb6eqHIlfNWsWml5epjyu1kAAKAQILi50KbjFzRl/UldiE20HasQ6KtJ3e5Wl3oV8rFlAACgMHC7odIRI0aoffv2GZ5bt26dmjRpIn9/f9WvX19bt27Ns3ZtOn5Bzy09aBfaJOlibKKeW3pQm45fyLO2AACAwsmtgtvbb7+t9957L8Nzy5cvV8+ePZWSkqIZM2aoTp066tq1q44ePZrr7Uq1Gpqy/qSMDM6lHZuy/qRSrRmVAAAAcIxbBLfk5GQNGzZMEydOVFBQULrz8fHxGjVqlO644w7t2rVLo0eP1sqVK9WyZUuNGTMm19u378zVdD1tf2dIuhCbqH1nruZ6WwAAQOHlFsHtxx9/1MaNG7V161bVrVs33fkdO3YoKipKI0eOVEBAgCTJZDJp2LBh+v7773XlypVcbd/l65mHtuyUAwAAyIhbTE648847dfz4cQUGBmZ4/tixY5Kk0NBQu+NNmzaV1WrV4cOH1bFjxwyvtVgsslgstu9xcXFOt69cCV+XlgMAAMiIW/S4BQcHZxraJCkmJkaSVL169XTXSdLZs2czvXbGjBkKDAy0fUJCQpxuX7NqpVUh0FeZLfph0q3Zpc2qlXa6bgAAgDRuEdxux2q1SpKKFy9ud9zf31+SdO3atUyvfeWVVxQbG2v7REZGOn1/Tw+TJnW7W5LShbe075O63c16bgAAIEcKRXDz9/eXyWSSYdjP2kz7npiY+btlZrNZAQEBdp/s6FKvgt4Pa6LygfbDoeUDffV+WBPWcQMAADnmFu+43U6lSpVkGIYiIyNVrVo12/GoqChJynYYc1aXehX0wN3l2TkBAADkikIR3Bo3bixJ2rlzp11w27dvnySpQoW86+3y9DDpvhpl8ux+AACg6CgUQ6VNmzZVlSpVNG/ePKWkpNiOL1y4UJ6enulmmwIAALijQhHcPDw8NGnSJP3000/q06ePvvvuOw0fPlwbN25UWFhYhov2AgAAuJtCMVQqSYMGDVJ0dLRef/11ffnll5Kkhx9+WHPnzs3nlgEAALiGyfjnVEw3d+nSJe3bt08VK1ZU06ZNnb4+Li5OgYGBio2NzbNJDQAAoGhzNH8Umh63NMHBwerWrVt+NwMAAMDlCsU7bgAAAEUBwQ0AAMBNENwAAADcBMENAADATRDcAAAA3ATBDQAAwE0Q3AAAANwEwQ0AAMBNENwAAADcBMENAADATRDcAAAA3ATBDQAAwE0Q3AAAANyEV343oKAxDEOSFBcXl88tAQAARUVa7kjLIZkhuP3D9evXJUkhISH53BIAAFDUXL9+XYGBgZmeNxm3i3ZFjNVq1fnz51WiRAmZTKb8bo6kWyk8JCREkZGRCggIyO/mFBr8XXMHf9fcwd/V9fib5g7+rtljGIauX7+uihUrysMj8zfZ6HH7Bw8PD1WuXDm/m5GhgIAA/keQC/i75g7+rrmDv6vr8TfNHfxdnZdVT1saJicAAAC4CYIbAACAmyC4uQGz2axJkybJbDbnd1MKFf6uuYO/a+7g7+p6/E1zB3/X3MXkBAAAADdBjxsAAICbILgBAAC4CYIbAACAmyC4odBITU1V8eLFZTKZ0n0OHz7sVF3PPvtshvWMGTMmV9qOoi06OloVKlRQmzZtbrvdTUZ4XpFbzp49m+Gz9fePM3hWc44FePNBamqqAgMDFR8fn+7coUOH1KhRI4frSkhI0KRJk/T555/r6tWreuihh/Tee+8pKCjIhS12D8eOHVN8fLymTJmi6tWr252rWrWqU3Xt3btXHTp00MCBA+2O33XXXTltpluLjo5WvXr1VLNmTf3www9O/0ub5zVjI0aMUExMjBYuXJitHVt4Xm85e/asqlWrlmUZZ4Ixz6tUtmxZffrppxmemzp1qtP/ocGzmnMEt3zgyoDRq1cvff3113rqqafUpEkTzZs3T927d9euXbuy3DKjMNq9e7c8PT31wgsvqFixYtmu58aNGzp+/LjmzZunsLAwF7bQ/eU0YPC8pvfll19q+fLlmj59uurUqeP09Tyv/+PqkMHzKhUrVizD52rPnj06deqUVq9e7XBdPKsuYiDPvffee4anp6dx48aNHNWzYsUKQ5Lxwgsv2I799ttvhqenp/HJJ5/ktJlup3///sa9996b43q2bdtmSDJOnDjhglYVHitXrjQkGdOnT8/W9Tyv6V26dMkICgoyGjZsaCQnJ2erDp7X29u9e7chyVi9erXD1/C8Zq1169ZG69atnbqGZ9U1CG75wFUBo1evXoaHh4dx+fJlu+OdO3c2unXrluP63U2tWrWMO++806hbt65hNpuNypUrG6NHjzauXr3qVD3Tp083vLy8jE6dOhmBgYFGiRIljM6dOxu7d+/OpZYXfK4IGDyv6fXs2dOQZMybN89YvXq1sX//fqfr4Hm9veyEDJ7XzH3zzTeGJGPnzp1OXcez6hpFo6+3gNm9e7euX7+uevXqydfXVyEhIRozZoxiYmKcqufYsWOqV6+eypYta3e8adOmOnjwoCubXOBFRUXp1KlTioqK0kMPPaQ5c+aoU6dOmjt3rrp27epUXeHh4UpJSVFAQIDefPNNvfjiizpy5Ijat2+vI0eO5NIvKNiGDRumK1euaOjQoVq/fr0OHDjgdB08r/a++uorrVmzRpI0fvx4DRgwQPfcc49CQ0N14cIFh+vhec3apk2btGvXLs2YMcOp63heM/f222+refPmat26tVPX8ay6SH4nx6Lm8uXLhiSjTJkyxksvvWT83//9nzFo0CDDZDIZLVq0cKqucuXKGT169Eh3fM6cOYbJZDIsFourml3gnTt3zpg6dapx6tQpu+PTpk0zJBlbtmxxuK4PP/zQWL58ud2x33//3TCbzUa/fv1c0l53sm7dOkOSIckoVqyYUaJECUOS0b59e+P8+fMO18Pzau/OO+80JBnvvvuukZSUZFitVmP16tWG2Ww27rvvPofr4XnNWmhoqNG8eXOnr+N5zdj+/fsNScbKlSudvpZn1TUIbnnMlQEjKCjICAsLS3f8ww8/NCQZUVFROW6vu0sLyi+++GKO6+ratatRtmxZF7TKvbgqYPC8/s8vv/xiSDLatGmT7tyoUaNc8h5QUX1e/y4nIYPnNWMDBw40ypcvb6SkpLisTp5V5zBUmscqVqyo119/XTVr1rQ7/swzz0iSvv32W4fr8vf3z/C48f9nTiUmJmazlYVH2uzS8+fPu6SuqKgoJScn57gud/Hrr7/ql19+UZs2bfTCCy/I29tbJpNJPXv21DPPPKPdu3fr5MmTDtXF8/o/0dHRkqSOHTumO3fnnXdKks6dO5ejexTF5/Wf5s2bp/Lly6tnz55OX8vzmt7Nmze1atUqPfnkk/L09HRZvTyrziG4FRDZCRiVKlVSREREuuNRUVGSpICAANc0zg0sXbpU/fv3T3f80KFDkqRy5co5VM+ZM2fUrVs3/fzzzxnWVapUKXl7e+essW7ElQGD5/V/KlWqJEkZLqly8eJFSVJwcPBt6+F5zVxOQwbPa3pr1qzR9evX1adPH6ev5Vl1HYJbHnNVwJCkxo0b6+DBg7p586bd8X379qlYsWJF6l8s8fHx+vTTT7Vt2zbbscTERE2YMEGS1L17d4fqKV26tLZu3aq33nrL7vj777+v33//3eF6CgtXBQyJ5/Xvqlatqtq1a2vFihV2vQw3b97Up59+qkqVKqlevXq3rYfnNXM5CRkSz2tGvvzySwUHB6t58+ZOX8uz6kL5PFRb5Pzf//2fIcn47rvvbMcSEhKMdu3aGZKM7du3O1zX1q1bDUnGnDlzbMf++usvw2w2G4888ogLW13wxcfHG7Vq1TKKFy9uDBo0yBg9erRRq1YtQ5IxYMAAW7lvv/32tu+7TJgwwZBkdOzY0Rg3bpzRtWtXQ5JRqVIlIzIyMpd/ScFTu3Zto169ekZSUpLtWHx8vHHHHXcYlSpVMlJTUx2qh+fV3oYNGwwvLy+jadOmxuzZs423337b9j7h0qVLDcPgec2JHj16GMHBwdm+nufVXnJyshEQEGA88cQTGZ7nWc07BLc85sqAYRiG0bZtW8PHx8eYO3eusXHjRqNevXqGyWSyC4ZFxblz54ynnnrKKF26tFGsWDGjWbNmxsKFCw2r1Wor065dO6NMmTJZ1mO1Wo3Zs2cbNWvWNHx8fIw77rjDeP75541Lly7l9k8okFwVMAyD5/Wfdu3aZXTv3t0oV66c4efnZzRo0MD44osvbOd5XrPHFSHDMHhe/+6HH36wrTmYEZ7VvENwyweuChiGcWth1M6dO9uWa/Dz88v0f1hAdrkiYBgGzyvyhitChmHwvKJgMhmGk5u3oUA6dOiQIiMj1axZM5UvXz6/mwNkiecV7oTnFQUJwQ0AAMBNMKsUAADATRDcAAAA3ATBDQAAwE0Q3AAAANwEwQ0AAMBNENwAFBkJCQnKrYn0N2/e1IoVK/TTTz/lqB7DMLRw4UKdOnXKRS0DUJgQ3AAUCr/++qtat26d5Z6HM2fOVOXKlTVt2jSX33/MmDF68skn9fbbb+eonkmTJmno0KHq3bu3kpKSbMdv3LihuLg4JSUlKTU1Ndv1r1271rY/amRkpLy8vHTPPffYzn/++ef666+/bN+nTZumsLAwbd++Pdv3BOA6BDcAuW748OGqWLGiatSooTp16ujOO+90+FOrVi1VrVpVZcuW1Z49ezK9h2EYCg8P15EjRzIt89133+n8+fOqUqWKy3/jgAEDJEkbN25UQkJCtusZM2aMqlSpooMHD+qVV16xOx4YGCiz2SwvLy+ZTKYsP717905X9+rVq9WzZ089/fTTkiRvb2+lpqbK29tbkhQVFaWBAweqZ8+etmvCw8O1bNky/fHHH9n+TQBcxyu/GwCg8EtMTNS1a9dksVhsoePvbty4ofj4eBUrVkzFixe3O5eamqrk5GQlJCRk2dOUFj78/PwyPH/jxg3t3btXFSpUUN++fbP1O8aOHZvpUKthGPL09FRCQoKGDBmismXLZliuZs2aGjlyZKb3KF26tD755BOFhobqP//5jzp16qTOnTurUqVKatKkifz8/OTl5SVPT890f0dJunDhgk6ePCkfH59053r27KkBAwbok08+0b333quwsDBJktlsliTNnz9fSUlJeuONN2zXHDp0SF5eXnrssccy/8MAyDv5ttkWAPx/48ePNyQZ48ePz3YdZ86cMSQZdevWzfD8Z599ZkgyXnrppWzfw9vb27ZvZXY/nTp1cuhevXv3Nh599FHj4MGDTrVx8eLFhiRjyJAhGZ6Pj4837rvvPmPJkiVGbGysIckIDQ01UlNTjXvvvddo3LixrezRo0cNSUbbtm2dagOA3MNQKYA8ER8fr4kTJ8pisTh13Z49e9SsWTPt3bs3R/f//PPPJUlz5sxR8eLFs/xk1kZvb28VK1ZMhmE4/blw4YKkzHsEhw0bptatW2vt2rWSpM8++0yrVq1S48aNs/V7M+pxe/755zVlyhTdf//9+uOPP2zv4509e1ZTp07Vgw8+qBYtWuhf//qXfvjhB23evFmS1Llz53R1paamKi4uLkfv2wFwHkOlAPJE7969tWHDBkVEROjjjz926BrDMDRmzBj99NNPGjdunMLDw+3O37x5U8nJySpWrFi66xISEpSUlKSSJUsqKipKmzdvVmBgoFq3bp3p/TZu3CiTyWQbOvwnHx8f24v92ZVZ3efOnVN4eLgGDx4sSRkOgzojo+v/+9//Zlj2zJkzmjJlit2xO+64Q998840kacKECZowYUKG1/7888+68847c9RWAI4juAHIE2+88Ya+++47ffLJJ2rQoIHGjRt322uWLVumvXv3ytfXV4sWLUp3/oMPPtDYsWPtjp04cUIeHrcGE2rUqKHff/9d8+bNU1JSksaPH6+pU6dmeK/k5GT5+PjI19c30/Z4eHjIarXq8OHDt237P0VHR0tSpj1UaeHz7z1yc+bM0ZgxY25b95w5c/T888/fttyxY8dkNptlNpv1zDPP2HrU2rZtq+XLlys+Pl5+fn6yWCyKj4/X6NGjFRgYqIEDB9rqiIiI0Jo1a9S8eXPVq1dPAQEBt70vANchuAHIE40aNdIHH3ygp556Si+99JLuu+8+3XfffZmWv3Dhgi3cvfnmm6pTp066Mr6+vqpQoYL8/f1lGIZOnz4tHx8f3XHHHbp586ZKly6tmzdvav78+fL29tZzzz0nwzAy7I1KGx69XXBLSEjI9vCldKuXMCNpPXFeXv/717K/v78kqXnz5mrRokW6a/bs2aO9e/fayt1OvXr1lJSUpGeffVabN2/W008/rcWLF8vLy0vjx4/XL7/8os2bN6tixYqaOXOmrFarunXrptmzZ9vq+Pzzz7VmzRr16tVL//rXvxz92QBchOAGIM+EhYVp3bp1+vLLL9W3b18dOXIkwx4bq9Wqvn37KioqSqGhoZn2Og0bNkzDhg2TdOs9rWrVqqlWrVo6fvy4rczrr7+u6OhoDRo0SH5+fmrXrp1eeeUVPfjgg3Z1pa2Zdrvg5uvrqzVr1mR4PjU1Vfv27dOlS5fUo0ePDMuULl06w+MZhcm0mbJdunTR5MmT052fPHmy9u7dayt3O8ePH9fTTz+t/fv3a9iwYXrrrbe0ePFiJScnq3v37lq6dKk6duyozZs365NPPpEknT9/3q6OtO/BwcEO3ROAaxHcAOSpWbNm6euvv1ZMTIxOnDiRYa/brFmztGPHDpUpU0affvpptt/3ioiI0Lvvviuz2azJkyfrxIkT2rt3rx577DFt375dzZs3t5VNC26ZTR6Q/rfkR5cuXTI8v27dOk2dOlXly5fX3LlzM5wg4Iy/975lxdPT87ZlFixYoFGjRskwDM2ePVujR4/WpUuXJN3qbXzsscc0e/ZsTZo0STNnztTPP/8sSTp69KhdPefOnZN06x04AHmPWaUA8lRISIjef/997d+/P9Oh0iFDhqhv375asmSJKlWqlO17ValSRV999ZX+/e9/KyQkRK1atdKiRYuUkJCgXr166fLly7ayjvS4Wa3WLO/3yCOPqF69erp48aLWrVuX7rxhGEpOTlZcXJzi4+Oz+auy59lnn9XLL7+sn376SaNHj1ZSUpImTpyoUaNG6c0335QkjRo1SidPntSmTZskSR07dtSVK1d09uxZWz1ps2Nr1aqVp+0HcAvBDUCuiYuLU0xMjBITE+0Wru3fv79q1qyZ6XUlS5bUsmXL1LVrV9ux1NRUWSwWXbt2TTdu3HC4DQ888IBGjBhh+96vXz8NHz5cxYsX1/Xr123H095xy6rHLSEhQfHx8VnuWJA2TPvEE0+kO+fh4SEfHx8FBgZqxowZDv+GKVOmZHivf84EvZ2pU6eqUaNGkqRt27bpgw8+0L59+9SmTRtbGT8/PzVt2lTdunWzTXjYtm2b7fyJEydUqlQplS9f3ql7A3ANhkoB5JpXX31V8+fPd7j8zJkzNXPmzNuWe+ONN/Taa69lu13vvvuuEhISdPr0afXs2VNr1qy5bY+b1WpVQkKC/Pz81KxZM7tzFy9e1K+//qpKlSplGUitVqtSUlKUkJCgcuXKOdze201OcMTmzZvVt29f26zStPB79uxZNWjQwK7sk08+qUWLFikmJkaenp7avHmzBg0apBs3bujEiRPp3g8EkHcIbgByTYUKFdSwYUP5+fllun6ZJJ0+fVqRkZEKCQlR9erVMyyTmpqq1NRUJSQkZLqdlKPMZrPmz5+vV199VampqTp16tRt33GLiYmRYRhq2LChduzYYXfuvffe04gRIzRgwABNnz49R23LyO0mJzgiJSVF8fHxslqtSkpKUnR0tG1Sw7Vr1yRJsbGxSkxMVNOmTWUymVS6dGm1adNG69evV1xcnH744QdZrVa7HjoAeYvgBiDXZLVw69+9/PLLmjlzpvr27au33nrLJfdOSkrS0aNHdc8999gd//333zVo0CDt3LlT1atX18cff6xatWrZAlBmPW5psykrV66c7lxMTIwkOdWLltceeughJSYmSpI+/fRT9e/fX2+88YbGjx8v6VZvYO3atXX58mU98MADtuv69eunHTt26MMPP7T9jUJDQ/P+BwCQxDtuAAqZmJgYDRkyRMHBwXrhhRdsxxMTEzV16lTVr19fu3bt0vDhw3X06FFb79Ht3nE7ceKEJGXYI/jbb79JksaMGZPl+28mk8luA/fbSUlJcWm5tLJvvPGGgoODNWrUKNvxNWvW6I8//tDw4cPt/gZhYWEqV66cpk+frvXr16tx48bphooB5B163AC4rVOnTun48ePavXu3vv32W0m3esY++ugjNWrUSL169ZIkrVixQi+++KIiIiJUvnx5LV68ON2SHrd7x+3QoUOSlOGWWWlDp7Vq1bLt2vBPly5d0rVr11SjRg2Hf19CQoKkW5MTspqIkNaT5ohly5bp1KlTkqTGjRurXbt2atWqlSZOnCh/f3+7sCvd+nsMHz7cNlQ7evRoh+8FwPUIbgDc1uOPP64jR45IurWA7X333afHHntMjz32mN06Y9evX1dERIQ6dOigL774QkFBQenqut07bmn7mP4zuH333XeKiIhQ3bp17Rb+/adevXpp1apVWU5e+Ke7777boaB01113OVzngAED1LZtW23dulXbtm3T+vXr9eGHH0qSypQpoyVLlmjw4MEqWbKk7Zq/rxPnTO8eANcjuAHId2nro91unbR/Gjx4sN566y0988wzGjhwoKpWrZppOR8fH/Xt2zfTxWojIyMlZRzcDhw4oBMnTqhVq1YqVaqU7bjFYrFt+zR8+PAs25o2nFq7du0Mz6cN1f79b9CuXTu1a9cuy3r/KW0v1KwWLa5WrZqGDh2qfv36KSwsTGvWrFGLFi10+vRp/etf/9L8+fP122+/yWQyadKkSZo+fbqqVq2qS5cu6ZlnnpHVatXQoUOdahcA1yC4Ach3aaElrdfLUUOHDtVzzz3n0A4DTz31lN33yMhIHTlyRF5eXoqKirKtq1ahQoV016bNFP17HVarVUOGDNHhw4fVsmVLPfvss5neOyYmRr/++qsqV65s15P1d2nLczj7N/g7q9VqW3Mtq10bLBaLPv/8c02cOFGRkZF6/vnn9Z///EdWq1Wff/65vLy89Ouvv+rZZ59VeHi46tSpo61bt+qHH35QWFiYnn32WUVERGjixIkOb7cFwDUIbgDyXVpYcTa0ZLXLwe2YTCZ1797dbmHgYsWK6bHHHrMrt23bNq1Zs0Z+fn564oknJMm29+lXX32lihUraunSpXY9eYmJiVqyZInMZrMtJCUlJdltsfVPOQlu0dHRaty4sa5cuWJ7L+7uu++2K3Pz5k1t2bJFmzZt0ooVK3T16lXVrVtXW7duVYcOHSTd2ou1VatWeuedd9S/f3+lpKSod+/eWrBggQICAtS3b19dv35dI0aM0LRp07RixQp98cUXaty4sdNtBpA9BDcA+S67wS0nKleurIYNGyo6Olo1atRQ8+bNNWDAgHTvoKW92zZixAiVKlVKa9eu1ZAhQxQdHa1KlSpp+/btqlatmt01vr6+WrJkicLDw23HvL297XZw+Ke0LbCy8zcoU6aM7r33Xq1evVrFixdX79691b9//3Tl3nvvPX377be67777NGbMGPXq1SvdZIrffvtNn3zyiapVq6bZs2fb7V4h3do6q27duurXr5/atm2rhg0bOt1eANlnMv7+n5sAgHS+/PJLtW/fXkFBQUpKStKjjz4qSVq0aFGma7ctX75cmzdvVkhIiGrVqqXWrVvn6sbsR44cUUJCgho3bpzpYscXLlxQTExMut64fzp8+LDq1q2b5TDotWvXbruwMgDXI7gBgJPStr4CgLxGcAMAAHAT7JwAAADgJghuAAAAboLgBgAA4CYIbgAAAG6C4AYAAOAmCG4AAABuguAGAADgJv4fjNwUSY5WOP4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Step 1:scikit-learn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = data[:, 5] # Get the number of rooms\n",
    "y = target # Get the target\n",
    "label_size = 18 # Label size\n",
    "ticklabel_size = 14 # Tick label size\n",
    "def normalization(x, norm_type='none'):\n",
    "    '''\n",
    "    Normalization\n",
    "    '''\n",
    "    if norm_type == 'min-max':\n",
    "        a, b = np.min(x), np.max(x)\n",
    "        x_norm = (x - a) / (b - a)\n",
    "\n",
    "    elif norm_type == 'z-score':\n",
    "        a, b = np.mean(x), np.std(x)\n",
    "        x_norm = (x - a) / b\n",
    "    \n",
    "    elif norm_type == 'none':\n",
    "        a, b = 0, 0\n",
    "        x_norm = x\n",
    "\n",
    "    return x_norm, a, b\n",
    "\n",
    "def norm_recover(x_norm, a, b, norm_type='none'):\n",
    "    '''\n",
    "    Normalization\n",
    "    '''\n",
    "    if norm_type == 'min-max':\n",
    "        x = x_norm * (b - a) + a\n",
    "\n",
    "    elif norm_type == 'z-score':\n",
    "        x = x_norm * b + a\n",
    "\n",
    "    elif norm_type == 'none':\n",
    "        x = x_norm\n",
    "\n",
    "    return x\n",
    "\n",
    "def draw_uniform_scatter(x, y, xlim, ylim):\n",
    "    '''\n",
    "    This a specific function to draw a scatter figure of room number and house price.\n",
    "    x: room number\n",
    "    y: house price\n",
    "    '''\n",
    "    global label_size, ticklabel_size # Set global variables of font size\n",
    "\n",
    "    fig, ax = plt.subplots() # Create a figure and a set of subplots.\n",
    "    ax.scatter(x, y) # Plot the data\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "    ax.set_xlabel('平均房间数', fontsize=label_size) # Label the x-axis\n",
    "    ax.set_ylabel('房价中位数（千美元）', fontsize=label_size) # Label the y-axis\n",
    "\n",
    "    ax.set_xlim(xlim[0], xlim[1]) # Set the x-axis limits\n",
    "    ax.set_ylim(ylim[0], ylim[1]) # Set the y-axis limits\n",
    "\n",
    "    ax.set_position([0.12, 0.14, 0.85, 0.83]) # Set the position of the figure\n",
    "\n",
    "    x_linear = np.linspace(xlim[0], xlim[1], 100) # Create a sequence of x to draw prediction function\n",
    "\n",
    "    return fig, ax, x_linear\n",
    "    \n",
    "norm_type = 'z-score'\n",
    "\n",
    "# Normalize x\n",
    "x_norm, x_a, x_b = normalization(x, norm_type=norm_type)\n",
    "\n",
    "# Split data into training and testing domains (Not for this section)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x_norm, y, test_size=0.2, random_state=42)\n",
    "num_samples = 10\n",
    "np.random.seed(42) # Set the random seed for reproducibility\n",
    "random_indices = np.random.choice(X_train.shape[0], num_samples, replace=False)\n",
    "x_train = X_train[random_indices]\n",
    "y_train = Y_train[random_indices]\n",
    "\n",
    "random_indices = np.random.choice(X_test.shape[0], num_samples, replace=False)\n",
    "x_test = X_test[random_indices]\n",
    "y_test = Y_test[random_indices]\n",
    "\n",
    "x_test_scatter = norm_recover(x_test, x_a, x_b, norm_type=norm_type)\n",
    "\n",
    "# Plot the training data\n",
    "x_lim_offset = 0.1\n",
    "x_train_scatter = norm_recover(x_train, x_a, x_b, norm_type=norm_type)\n",
    "x_scatter_min = np.min([np.min(x_train_scatter), np.min(x_test_scatter)]) - x_lim_offset\n",
    "x_scatter_max = np.max([np.max(x_train_scatter), np.max(x_test_scatter)]) + x_lim_offset\n",
    "x_lim = [x_scatter_min, x_scatter_max]\n",
    "\n",
    "y_lim_offset = 1\n",
    "y_scatter_min = np.min([np.min(y_train), np.min(y_test)]) - y_lim_offset\n",
    "y_scatter_max = np.max([np.max(y_train), np.max(y_test)]) + y_lim_offset\n",
    "y_lim = [y_scatter_min, y_scatter_max]\n",
    "\n",
    "fig, ax, _ = draw_uniform_scatter(x_train_scatter, y_train, x_lim, y_lim)\n",
    "plt.savefig(f'scatter_train_data_{num_samples}.png', dpi=300) # Make figure clearer\n",
    "plt.show()\n",
    "\n",
    "# Plot the testing data\n",
    "fig, ax, _ = draw_uniform_scatter(x_test_scatter, y_test, x_lim, y_lim)\n",
    "plt.savefig(f'scatter_test_data_{num_samples}.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a99b4b1-8a31-4448-be29-bdbfc4c590ca",
   "metadata": {},
   "source": [
    "### 步骤3 从训练集中生成不同体量的子集，分别训练模型并预测房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7ed23a3b-cecb-4565-8beb-7f06e69b7032",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_poly_mdl(x_tr, y_tr, x_te, y_te, lr=100, max_epoch=1000000, batch_size=1, p=0, regulization_method='none', regulization_lambda=0, disp=False, stop=False):\n",
    "    '''\n",
    "    Learning weight w and bias b\n",
    "    p is the order of polynomial\n",
    "\n",
    "    Return:\n",
    "    w_list: array of w\n",
    "    b_list: array of b\n",
    "    err_train: prediction accuracy\n",
    "    '''\n",
    "    global norm_type\n",
    "\n",
    "    # Train logs\n",
    "    logs = {}\n",
    "    logs['epoch_itr'] = []\n",
    "    logs['loss_train'] = []\n",
    "    logs['loss_test'] = []\n",
    "    logs['loss_batch'] = []\n",
    "    logs['err_train'] = []\n",
    "    logs['err_test'] = []\n",
    "\n",
    "    # Initialize w, b, and exponential x\n",
    "    b = 0.0\n",
    "    if p > 0:\n",
    "        w = np.random.randn(p)\n",
    "        \n",
    "        x_tr = [x_tr ** (i+1) for i in range(p)]\n",
    "        x_tr = np.array(x_tr).T\n",
    "        \n",
    "        x_te = [x_te ** (i+1) for i in range(p)]\n",
    "        x_te = np.array(x_te).T\n",
    "        \n",
    "    else:\n",
    "        w = np.random.randn(x_tr.shape[1])\n",
    "\n",
    "    # Initialize learning rate\n",
    "    lr_w = np.zeros(w.size)\n",
    "    lr_b = 0.0\n",
    "\n",
    "    # For early stop\n",
    "    min_err = 100000000\n",
    "    err_no_improvement = 0\n",
    "    early_stop = 100\n",
    "\n",
    "    # Model training process\n",
    "    itr_num = 0\n",
    "    for ie in range(max_epoch):\n",
    "\n",
    "        idx = np.random.permutation(x_tr.shape[0]) # Shuffle x\n",
    "\n",
    "        dw = np.zeros(w.size)\n",
    "        db = 0.0\n",
    "\n",
    "        for ib in range(0, x_tr.shape[0], batch_size):\n",
    "            batch_idx = idx[ib:ib+batch_size]\n",
    "            x_batch = x_tr[batch_idx, :]\n",
    "            y_batch = y_tr[batch_idx]\n",
    "\n",
    "            y_batch_pred = np.matmul(x_batch, w) + b\n",
    "            y_batch_diff = y_batch_pred - y_batch\n",
    "            \n",
    "            # Regularization in loss and gradient\n",
    "            if regulization_method == 'L1':\n",
    "                loss_reg = regulization_lambda * np.sum(np.abs(w))\n",
    "                grad_reg = regulization_lambda * np.sign(w)\n",
    "            elif regulization_method == 'L2':\n",
    "                loss_reg = regulization_lambda * np.sum(w ** 2) / 2\n",
    "                grad_reg = regulization_lambda * w\n",
    "            else:\n",
    "                loss_reg = 0.0\n",
    "                grad_reg = np.zeros(w.size)\n",
    "\n",
    "            logs['loss_batch'].append(np.mean(y_batch_diff ** 2) / 2 + loss_reg)\n",
    "\n",
    "            # Compute partial derivative of w and b\n",
    "            for i in range(w.size):\n",
    "                dw[i] = dw[i] + np.mean(y_batch_diff * x_batch[:, i]) + grad_reg[i]\n",
    "            db = db + np.mean(y_batch_diff * 1.0) # Compute partial derivative of b\n",
    "            \n",
    "            itr_num += 1\n",
    "\n",
    "        # Regularization in loss and gradient\n",
    "        if regulization_method == 'L1':\n",
    "            loss_reg = regulization_lambda * np.sum(np.abs(w))\n",
    "            \n",
    "        elif regulization_method == 'L2':\n",
    "            loss_reg = regulization_lambda * np.sum(w ** 2) / 2\n",
    "            \n",
    "        else:\n",
    "            loss_reg = 0\n",
    "        \n",
    "        # Compute epoch loss and err\n",
    "        y_tr_pred = np.matmul(x_tr, w) + b\n",
    "        y_tr_diff = y_tr_pred - y_tr\n",
    "        \n",
    "        loss_train = np.mean(y_tr_diff ** 2) / 2 + loss_reg\n",
    "        logs['loss_train'].append(loss_train)\n",
    "        \n",
    "        err_train = np.mean(np.abs(y_tr_diff))\n",
    "        logs['err_train'].append(err_train)\n",
    "                \n",
    "        y_te_pred = np.matmul(x_te, w) + b\n",
    "        y_te_diff = y_te_pred - y_te\n",
    "        \n",
    "        loss_test = np.mean(y_te_diff ** 2) / 2 + loss_reg\n",
    "        logs['loss_test'].append(loss_test)\n",
    "        \n",
    "        err_test = np.mean(np.abs(y_te_diff))\n",
    "        logs['err_test'].append(err_test)\n",
    "        \n",
    "        # Display training process\n",
    "        if (ie + 1) % 10000 == 0 and disp == True:\n",
    "            print(f'Epoch {ie+1}: train loss ({loss_train:.8f}), test loss ({loss_test:.8f}), train error ({err_train:.4f}), test error ({err_test:.4f})')\n",
    "        \n",
    "        # Early stop\n",
    "        if stop == True:\n",
    "            if err_test < min_err:\n",
    "                min_err = err_test\n",
    "                err_no_improvement = 0\n",
    "            else:\n",
    "                err_no_improvement += 1\n",
    "\n",
    "            if err_no_improvement >= early_stop:\n",
    "                break\n",
    "\n",
    "        # Adagrad\n",
    "        lr_w = lr_w + dw ** 2\n",
    "        lr_b = lr_b + db ** 2\n",
    "\n",
    "        w -= lr / np.sqrt(lr_w) * dw # Update w\n",
    "        b -= lr / np.sqrt(lr_b) * db # Update d\n",
    "\n",
    "    # Regularization in loss and gradient\n",
    "    if regulization_method == 'L1':\n",
    "        loss_reg = regulization_lambda * np.sum(np.abs(w))\n",
    "        \n",
    "    elif regulization_method == 'L2':\n",
    "        loss_reg = regulization_lambda * np.sum(w ** 2) / 2\n",
    "        \n",
    "    else:\n",
    "        loss_reg = 0\n",
    "\n",
    "    # Compute epoch loss and err\n",
    "    y_tr_pred = np.matmul(x_tr, w) + b\n",
    "    y_tr_diff = y_tr_pred - y_tr\n",
    "\n",
    "    loss_train = np.mean(y_tr_diff ** 2) / 2 + loss_reg\n",
    "    logs['loss_train'].append(loss_train)\n",
    "    \n",
    "    err_train = np.mean(np.abs(y_tr_diff))\n",
    "    logs['err_train'].append(err_train)\n",
    "            \n",
    "    y_te_pred = np.matmul(x_te, w) + b\n",
    "    y_te_diff = y_te_pred - y_te\n",
    "\n",
    "    loss_test = np.mean(y_te_diff ** 2) / 2 + loss_reg\n",
    "    logs['loss_test'].append(loss_test)\n",
    "    \n",
    "    err_test = np.mean(np.abs(y_te_diff))\n",
    "    logs['err_test'].append(err_test)\n",
    "    \n",
    "    # Change logs to array\n",
    "    logs['loss_train'] = np.array(logs['loss_train'])\n",
    "    logs['err_train'] = np.array(logs['err_train'])\n",
    "    logs['loss_test'] = np.array(logs['loss_test'])\n",
    "    logs['err_test'] = np.array(logs['err_test'])\n",
    "\n",
    "    return w, b, logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b69c20b2-a6ae-44bb-af34-022c84130ed4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p=1\t Training avg_err: 2.8898418256399423\t Testing avg_err: 4.9989056220327\n",
      "p=2\t Training avg_err: 2.86878856935306\t Testing avg_err: 5.1824471458777435\n",
      "p=3\t Training avg_err: 2.4947969414931563\t Testing avg_err: 9.776274245103085\n",
      "p=4\t Training avg_err: 1.830002792195467\t Testing avg_err: 27.693371711085614\n",
      "p=5\t Training avg_err: 1.8543885582291835\t Testing avg_err: 26.20848282537674\n",
      "p=6\t Training avg_err: 1.865461731254981\t Testing avg_err: 47.84924749227257\n",
      "p=7\t Training avg_err: 1.8404993838474106\t Testing avg_err: 51.925377284213916\n",
      "p=8\t Training avg_err: 1.8578458989679905\t Testing avg_err: 110.66481700647628\n",
      "p=9\t Training avg_err: 1.8502866065808696\t Testing avg_err: 123.11731350698408\n",
      "p=10\t Training avg_err: 1.9959962486236467\t Testing avg_err: 145.83850373088586\n"
     ]
    }
   ],
   "source": [
    "p_list = range(1, 11, 1) # Set the order of polynomial\n",
    "param_list = []\n",
    "err_list = []\n",
    "\n",
    "for p in p_list:\n",
    "    # Train model\n",
    "    w, b, logs = train_poly_mdl(x_train, y_train, x_test, y_test, p=p, lr=100, max_epoch=10000, batch_size=num_samples)\n",
    "    param_list.append((w, b))\n",
    "\n",
    "    err_train = logs['err_train'][-1]\n",
    "    err_test = logs['err_test'][-1]\n",
    "    err_list.append((err_train, err_test))\n",
    "\n",
    "    print(f'p={p}\\t Training avg_err: {err_train}\\t Testing avg_err: {err_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "dd6d0700-2093-40bc-98de-fb6e8de3edec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_list = np.array(err_list)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plt.plot(p_list, err_list[:, 0], color='blue', marker='.', markersize=10, linewidth=2, zorder=1, label='训练集') # Plot training losses\n",
    "plt.plot(p_list, err_list[:, 1], color='red', marker='.', markersize=10, linewidth=2, zorder=1, label='测试集') # Plot training losses\n",
    "plt.legend(ncol=1, fontsize=label_size)\n",
    "\n",
    "ax.set_xticks(p_list)\n",
    "ax.set_ylim(0, 25)\n",
    "\n",
    "ax.set_xlabel('预测模型复杂度', fontsize=label_size)\n",
    "ax.set_ylabel('房价预测误差（千美元）', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# plt.savefig('complexity_vs_error.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4d1a18e3-3e52-4f81-a981-97f01dfc9adc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p=5\t Training avg_err: 1.854755515271385\t Testing avg_err: 26.15535821181722\n",
      "p=6\t Training avg_err: 1.8647601688263862\t Testing avg_err: 47.9324938987112\n",
      "p=7\t Training avg_err: 1.8462584307782812\t Testing avg_err: 50.8167562564209\n",
      "p=8\t Training avg_err: 1.8156308359229485\t Testing avg_err: 463.6367283859839\n",
      "p=9\t Training avg_err: 1.8548271018211515\t Testing avg_err: 91.99703960799093\n",
      "p=10\t Training avg_err: 1.8732933790586983\t Testing avg_err: 149.21588395824378\n",
      "p=11\t Training avg_err: 1.8524521073791618\t Testing avg_err: 204.54260940831836\n",
      "p=12\t Training avg_err: 1.8750388646904064\t Testing avg_err: 548.6939525405435\n",
      "p=13\t Training avg_err: 1.8677271242925193\t Testing avg_err: 1737.9968169209037\n",
      "p=14\t Training avg_err: 5.641381386638649\t Testing avg_err: 10725.763412401935\n",
      "p=15\t Training avg_err: 1.8374437044408913\t Testing avg_err: 7643.653139605686\n",
      "p=16\t Training avg_err: 1.950205797816772\t Testing avg_err: 3477.9890563767467\n",
      "p=17\t Training avg_err: 1.8102044334213816\t Testing avg_err: 23138.80895769837\n"
     ]
    }
   ],
   "source": [
    "p_list = range(5, 18, 1) # Set the order of polynomial\n",
    "param_list = []\n",
    "err_list = []\n",
    "\n",
    "for p in p_list:\n",
    "    # Train model\n",
    "    w, b, logs = train_poly_mdl(x_train, y_train, x_test, y_test, p=p, lr=100, max_epoch=10000, batch_size=num_samples)\n",
    "    param_list.append((w, b))\n",
    "\n",
    "    err_train = logs['err_train'][-1]\n",
    "    err_test = logs['err_test'][-1]\n",
    "    err_list.append((err_train, err_test))\n",
    "\n",
    "    print(f'p={p}\\t Training avg_err: {err_train}\\t Testing avg_err: {err_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1a8cae90-593e-41c0-8397-19d96a1185ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_list = np.array(err_list)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plt.plot(p_list, err_list[:, 0], color='blue', marker='.', markersize=10, linewidth=2, zorder=1, label='训练集') # Plot training losses\n",
    "plt.plot(p_list, err_list[:, 1], color='red', marker='.', markersize=10, linewidth=2, zorder=1, label='测试集') # Plot training losses\n",
    "plt.legend(ncol=1, fontsize=label_size)\n",
    "\n",
    "ax.set_xticks(p_list)\n",
    "ax.set_ylim(0, 25)\n",
    "\n",
    "ax.set_xlabel('预测模型复杂度', fontsize=label_size)\n",
    "ax.set_ylabel('房价预测误差（千美元）', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# plt.savefig('complexity_vs_error.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d798df-905a-4e78-96e3-78280953bf25",
   "metadata": {},
   "source": [
    "### 步骤4 使用正则化方法训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "bde6883d-ff0d-4dac-a66b-d58cb16a7af3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10000: train loss (12.29023252), test loss (20.55758998), train error (3.5929), test error (5.3799)\n",
      "Epoch 20000: train loss (11.83127731), test loss (19.04378525), train error (3.4639), test error (5.1317)\n",
      "Epoch 30000: train loss (11.53735739), test loss (18.74243240), train error (3.4233), test error (5.0317)\n",
      "Epoch 40000: train loss (11.30095511), test loss (18.64106982), train error (3.3971), test error (4.9645)\n",
      "Epoch 50000: train loss (11.10411156), test loss (18.57281723), train error (3.3768), test error (4.9072)\n",
      "Epoch 60000: train loss (10.93641434), test loss (18.50446490), train error (3.3608), test error (4.8550)\n",
      "Epoch 70000: train loss (10.79064460), test loss (18.42881831), train error (3.3451), test error (4.8064)\n",
      "Epoch 80000: train loss (10.66171851), test loss (18.34472071), train error (3.3298), test error (4.7609)\n",
      "Epoch 90000: train loss (10.54602914), test loss (18.25278912), train error (3.3147), test error (4.7182)\n",
      "Epoch 100000: train loss (10.44099460), test loss (18.15427443), train error (3.2999), test error (4.6778)\n",
      "Lambda=0\t Training avg_err: 3.299942721576948\t Testing avg_err: 4.677780914506547\n",
      "Epoch 10000: train loss (16.15957269), test loss (26.04514941), train error (3.9143), test error (5.5852)\n",
      "Epoch 20000: train loss (15.02660838), test loss (23.70983691), train error (3.7807), test error (5.4094)\n",
      "Epoch 30000: train loss (14.13447933), test loss (21.89831326), train error (3.6808), test error (5.2558)\n",
      "Epoch 40000: train loss (13.43163625), test loss (20.47632552), train error (3.5979), test error (5.1196)\n",
      "Epoch 50000: train loss (12.87771086), test loss (19.36005521), train error (3.5243), test error (4.9987)\n",
      "Epoch 60000: train loss (12.44102236), test loss (18.48396636), train error (3.4590), test error (4.8913)\n",
      "Epoch 70000: train loss (12.09667827), test loss (17.79662267), train error (3.4010), test error (4.7961)\n",
      "Epoch 80000: train loss (11.82510153), test loss (17.25762046), train error (3.3495), test error (4.7115)\n",
      "Epoch 90000: train loss (11.61088410), test loss (16.83520376), train error (3.3072), test error (4.6363)\n",
      "Epoch 100000: train loss (11.44189210), test loss (16.50440325), train error (3.2730), test error (4.5696)\n",
      "Lambda=1\t Training avg_err: 3.2730445487826345\t Testing avg_err: 4.56959583555203\n",
      "Epoch 10000: train loss (21.95659160), test loss (28.96921178), train error (4.2127), test error (5.2257)\n",
      "Epoch 20000: train loss (18.90254925), test loss (25.21756381), train error (3.8754), test error (5.0721)\n",
      "Epoch 30000: train loss (17.40643907), test loss (23.38824310), train error (3.6621), test error (4.9648)\n",
      "Epoch 40000: train loss (16.67171450), test loss (22.49580689), train error (3.5432), test error (4.8896)\n",
      "Epoch 50000: train loss (16.31046296), test loss (22.06114906), train error (3.4750), test error (4.8369)\n",
      "Epoch 60000: train loss (16.13273655), test loss (21.85020574), train error (3.4373), test error (4.7999)\n",
      "Epoch 70000: train loss (16.04527432), test loss (21.74842751), train error (3.4121), test error (4.7740)\n",
      "Epoch 80000: train loss (16.00222649), test loss (21.69975757), train error (3.3955), test error (4.7558)\n",
      "Epoch 90000: train loss (15.98103738), test loss (21.67680005), train error (3.3852), test error (4.7430)\n",
      "Epoch 100000: train loss (15.97060727), test loss (21.66620022), train error (3.3780), test error (4.7341)\n",
      "Lambda=10\t Training avg_err: 3.378032801465577\t Testing avg_err: 4.734088524395375\n",
      "Epoch 10000: train loss (30.51870313), test loss (29.07129865), train error (5.1385), test error (5.7752)\n",
      "Epoch 20000: train loss (26.48065907), test loss (28.70034805), train error (4.8443), test error (5.9927)\n",
      "Epoch 30000: train loss (25.81297890), test loss (29.37142604), train error (4.8043), test error (6.1117)\n",
      "Epoch 40000: train loss (25.70217418), test loss (29.78087732), train error (4.8002), test error (6.1602)\n",
      "Epoch 50000: train loss (25.68377446), test loss (29.97031813), train error (4.8013), test error (6.1800)\n",
      "Epoch 60000: train loss (25.68071878), test loss (30.05127058), train error (4.8017), test error (6.1881)\n",
      "Epoch 70000: train loss (25.68021131), test loss (30.08488362), train error (4.8019), test error (6.1913)\n",
      "Epoch 80000: train loss (25.68012703), test loss (30.09868520), train error (4.8020), test error (6.1927)\n",
      "Epoch 90000: train loss (25.68011304), test loss (30.10432685), train error (4.8020), test error (6.1932)\n",
      "Epoch 100000: train loss (25.68011071), test loss (30.10662881), train error (4.8020), test error (6.1934)\n",
      "Lambda=100\t Training avg_err: 4.802013174538789\t Testing avg_err: 6.193437278988246\n",
      "Epoch 10000: train loss (31.53045270), test loss (34.78556063), train error (5.7611), test error (6.7399)\n",
      "Epoch 20000: train loss (30.79797101), test loss (35.23239175), train error (5.7528), test error (6.9649)\n",
      "Epoch 30000: train loss (30.78883444), test loss (35.33948806), train error (5.7544), test error (6.9919)\n",
      "Epoch 40000: train loss (30.78872039), test loss (35.35216723), train error (5.7546), test error (6.9949)\n",
      "Epoch 50000: train loss (30.78871897), test loss (35.35359275), train error (5.7546), test error (6.9953)\n",
      "Epoch 60000: train loss (30.78871895), test loss (35.35375213), train error (5.7546), test error (6.9953)\n",
      "Epoch 70000: train loss (30.78871895), test loss (35.35376994), train error (5.7546), test error (6.9953)\n",
      "Epoch 80000: train loss (30.78871895), test loss (35.35377193), train error (5.7546), test error (6.9953)\n",
      "Epoch 90000: train loss (30.78871895), test loss (35.35377215), train error (5.7546), test error (6.9953)\n",
      "Epoch 100000: train loss (30.78871895), test loss (35.35377217), train error (5.7546), test error (6.9953)\n",
      "Lambda=1000\t Training avg_err: 5.754608375711934\t Testing avg_err: 6.9953182522271735\n",
      "Epoch 10000: train loss (35.39562425), test loss (41.73468315), train error (6.2958), test error (7.1037)\n",
      "Epoch 20000: train loss (34.07587897), test loss (41.16110076), train error (6.1393), test error (7.2994)\n",
      "Epoch 30000: train loss (34.05637667), test loss (41.21456636), train error (6.1242), test error (7.3232)\n",
      "Epoch 40000: train loss (34.05608824), test loss (41.22288948), train error (6.1223), test error (7.3261)\n",
      "Epoch 50000: train loss (34.05608398), test loss (41.22392861), train error (6.1221), test error (7.3265)\n",
      "Epoch 60000: train loss (34.05608391), test loss (41.22405537), train error (6.1221), test error (7.3265)\n",
      "Epoch 70000: train loss (34.05608391), test loss (41.22407080), train error (6.1221), test error (7.3265)\n",
      "Epoch 80000: train loss (34.05608391), test loss (41.22407267), train error (6.1221), test error (7.3265)\n",
      "Epoch 90000: train loss (34.05608391), test loss (41.22407290), train error (6.1221), test error (7.3265)\n",
      "Epoch 100000: train loss (34.05608391), test loss (41.22407293), train error (6.1221), test error (7.3265)\n",
      "Lambda=10000\t Training avg_err: 6.122083527516985\t Testing avg_err: 7.326510180989089\n",
      "Epoch 10000: train loss (38.77261942), test loss (49.01053020), train error (6.7197), test error (7.9165)\n",
      "Epoch 20000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 30000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 40000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 50000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 60000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 70000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 80000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 90000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Epoch 100000: train loss (38.77261940), test loss (49.01088285), train error (6.7197), test error (7.9166)\n",
      "Lambda=100000\t Training avg_err: 6.71973713317799\t Testing avg_err: 7.916605346456068\n"
     ]
    }
   ],
   "source": [
    "lambda_list = [0, 1, 10, 100, 1000, 10000, 100000]\n",
    "\n",
    "b_list = []\n",
    "w_list = []\n",
    "err_list = []\n",
    "loss_list = []\n",
    "\n",
    "for lbd in lambda_list:\n",
    "    # Train model\n",
    "    w, b, logs = train_poly_mdl(x_tr1, y_tr1, x_te1, y_te1, lr=1, max_epoch=100000, batch_size=10, disp=True, regulization_method='L2', regulization_lambda=lbd)\n",
    "    b_list.append(b)\n",
    "    w_list.append(w)\n",
    "    \n",
    "    err_train = logs['err_train'][-1]\n",
    "    err_test = logs['err_test'][-1]\n",
    "    err_list.append([err_train, err_test])\n",
    "    \n",
    "    loss_train = logs['loss_train'][-1]\n",
    "    loss_test = logs['loss_test'][-1]\n",
    "    loss_list.append([loss_train, loss_test])\n",
    "\n",
    "    print(f'Lambda={lbd}\\t Training avg_err: {err_train}\\t Testing avg_err: {err_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5a9630ef-9c7b-4546-bd58-2492282df9de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\26463\\AppData\\Local\\Temp\\ipykernel_19448\\3506824197.py:33: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  colormap = plt.cm.get_cmap('jet', 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_array = np.array(b_list)\n",
    "w_array = np.array(w_list)\n",
    "err_array = np.array(err_list)\n",
    "loss_array = np.array(loss_list)\n",
    "\n",
    "# Draw errors vs lambda\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "xticks = np.arange(len(lambda_list))\n",
    "xticklabels = lambda_list\n",
    "\n",
    "plt.plot(xticks, err_array[:, 0], color='blue', marker='.', markersize=10, linewidth=2, zorder=1, label='Train') # Plot training losses\n",
    "plt.plot(xticks, err_array[:, 1], color='red', marker='.', markersize=10, linewidth=2, zorder=1, label='Test') # Plot training losses\n",
    "plt.legend(ncol=1, fontsize=label_size)\n",
    "\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xticklabels(xticklabels)\n",
    "# ax.set_ylim(0, 25)\n",
    "\n",
    "ax.set_xlabel('Lambda', fontsize=label_size)\n",
    "ax.set_ylabel('Prediction Error', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "plt.savefig('lambda_2_vs_error.png', dpi=300) # Make figure clearer\n",
    "plt.show()\n",
    "\n",
    "# Draw weights changes\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "xticks = np.arange(len(lambda_list))\n",
    "xticklabels = lambda_list\n",
    "\n",
    "colormap = plt.cm.get_cmap('jet', 12)\n",
    "linestyles = ['-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "markers = ['o', 's', '^', 'v', '*', 'x', '+', 'D', 'h', 'p', '<', '>']\n",
    "for i in range(w_array.shape[1]):\n",
    "    plt.plot(xticks, w_array[:, i], color=colormap(i), linestyle=linestyles[i], marker=markers[i], markersize=7, linewidth=2, zorder=1, label=f'w{i+1}') # Plot training losses\n",
    "plt.legend(ncol=4, fontsize=label_size, loc='upper right')\n",
    "\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xticklabels(xticklabels)\n",
    "# ax.set_ylim(0, 25)\n",
    "\n",
    "ax.set_xlabel('Lambda', fontsize=label_size)\n",
    "ax.set_ylabel('Weight Value', fontsize=label_size)\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "plt.savefig('lambda_2_vs_weights.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d2eec1-6438-4d0e-9ed0-e2a89b205ba1",
   "metadata": {},
   "source": [
    "### 步骤5 交叉验证评估模型泛化能力"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741c49ed-5bec-41dc-9074-4c3a3c6b867c",
   "metadata": {},
   "source": [
    "#### 留出法（Leave-p-out）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "634e2479-1877-46f4-ac8d-e167233df3ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def leave_p_out(data, target, p, num_rounds):\n",
    "    err_train = []\n",
    "    err_val = []\n",
    "    for _ in range(num_rounds):\n",
    "        indices = np.random.permutation(len(data))\n",
    "        train_idx = indices[p:]\n",
    "        val_idx = indices[:p]\n",
    "        X_train, X_val = data[train_idx], data[val_idx]\n",
    "        y_train, y_val = target[train_idx], target[val_idx]\n",
    "        model = LinearRegression().fit(X_train, y_train)\n",
    "        y_train_pred = model.predict(X_train)\n",
    "        y_val_pred = model.predict(X_val)\n",
    "        err_train.append(mean_squared_error(y_train, y_train_pred))\n",
    "        err_val.append(mean_squared_error(y_val, y_val_pred))\n",
    "    return err_train, err_val\n",
    "\n",
    "# 参数设置\n",
    "p = 10\n",
    "num_rounds = 30\n",
    "err_train_leave_p_out, err_val_leave_p_out = leave_p_out(data, target, p, num_rounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f52def-e280-4a5b-81c8-8caf9aea83ba",
   "metadata": {},
   "source": [
    "#### K折法（K-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0c620960-8135-4d01-bb7c-26565724818a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def k_fold(data, target, k, num_rounds):\n",
    "    err_train = []\n",
    "    err_val = []\n",
    "    kf = KFold(n_splits=k, shuffle=True)\n",
    "    for _ in range(num_rounds):\n",
    "        for train_index, val_index in kf.split(data):\n",
    "            X_train, X_val = data[train_index], data[val_index]\n",
    "            y_train, y_val = target[train_index], target[val_index]\n",
    "            model = LinearRegression().fit(X_train, y_train)\n",
    "            y_train_pred = model.predict(X_train)\n",
    "            y_val_pred = model.predict(X_val)\n",
    "            err_train.append(mean_squared_error(y_train, y_train_pred))\n",
    "            err_val.append(mean_squared_error(y_val, y_val_pred))\n",
    "    return err_train, err_val\n",
    "\n",
    "k = 5\n",
    "err_train_k_fold, err_val_k_fold = k_fold(data, target, k, num_rounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08706db1-d15a-467a-9727-0bbdff792b2b",
   "metadata": {},
   "source": [
    "#### 自助法（Bootstrapping）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "25a659bc-d05b-41b9-acf9-f72993898ee3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bootstrapping(data, target, num_samples, num_rounds):\n",
    "    err_train = []\n",
    "    err_val = []\n",
    "    for _ in range(num_rounds):\n",
    "        train_idx = np.random.choice(len(data), num_samples, replace=True)\n",
    "        val_idx = np.random.choice(len(data), num_samples, replace=True)\n",
    "        X_train, X_val = data[train_idx], data[val_idx]\n",
    "        y_train, y_val = target[train_idx], target[val_idx]\n",
    "        model = LinearRegression().fit(X_train, y_train)\n",
    "        y_train_pred = model.predict(X_train)\n",
    "        y_val_pred = model.predict(X_val)\n",
    "        err_train.append(mean_squared_error(y_train, y_train_pred))\n",
    "        err_val.append(mean_squared_error(y_val, y_val_pred))\n",
    "    return err_train, err_val\n",
    "\n",
    "num_samples = 100\n",
    "err_train_bootstrapping, err_val_bootstrapping = bootstrapping(data, target, num_samples, num_rounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "d8c347eb-e43c-4efe-a3da-f3d50d7f8ac7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SiZKSEuTk5Bz3PhFTECUlJQEQ/+jk5GSNR0NERERERFqpqalBbm6uWiMcT8QUREqbXHJyMgsiIiIiIiLyaykNQxWIiIiIiChqsSAiIiIiIqKoxYKIiIiIiIiiVsSsISIiIiKi8JJlGa2trXC5XFoPhaKQ1WqF2Wzu9nlYEBERERFRwJqbm3HgwAE0NDRoPRSKUpIkIScnB4mJid06DwsiIiIiIgqI2+3Gnj17YDab0bt3b9hsNr/SvIiCRZZllJeXo7S0FP379+/WTBELIiIiIiIKSHNzM9xuN3JzcxEfH6/1cChKZWZmori4GC0tLd0qiBiqQERERERdYjLxpSRpJ1izkvwuJiIiIiKiqMWCiIiIiIiIohYLIiIiIiKKCosWLYIkScd8fP31190+d3FxcZdauLr6uEC1928fMmRIyJ/XCBiqQERERERR4ZprrsGUKVOwadMmjBs3DlVVVQDQ7dhmAOjTp496vnA8riuGDBmCFStWqJ8HYw+fSMCCiIiIiIi6TZZlNLZos0FrnNXs1yyLzWaDzWZDUlISACAlJSVoYzCZTF06X1cf1xVmszlsz2UkLIiIiIiIqNsaW1wY9MfPNHnuLX86H/G27r+snTZtGvLz89GvXz/8+c9/xqxZs3DnnXcCAFasWIFZs2Zh+/btGDx4MF555ZU2LWfFxcUoKCiALMvqdV9//TWmTZuGp556CrNmzUJNTQ3mzJmDu+66q1uP27hxI6655hocPHgQN9xwAz755BPccccdmDVrVpf/7fn5+XjppZewbNkyvPzyy/jss88wfPjwTm97++238dBDD6GiogJTp07F3/72N8TGxnb6OD3hGiIiIiIiIo/PPvsMzz77LObPn48pU6YAEBvRXnnllbjqqquwe/dujB07FrNnz/brfBUVFZg3bx4+/vhjzJkzB7Nnz0ZjY2O3Hjdjxgxce+21WLZsGRYuXIhXXnkF1113Xafn3LRpE1JSUtSP//u//2tz+x/+8AeUlZXhP//5D/r27dvpbWvXrsWNN96Ixx57DN9++y3Wrl2L+++/3+9z6gVniIiIiIio2+KsZmz50/maPXew7N69G9u3b4fdbm9z/YYNG2C327Fx40bU1tZi+/btfp2vrq4Ozz33HIYMGYIBAwbg17/+NQ4fPoy8vLwuP279+vVYtGgRBgwYgEGDBqG4uBinnXZap2M54YQT8Mknn6ifH90+Z7fbsWjRonYf295tL774Iq699lq1cJw/fz4mTJiAJ554Qm1hPN459YIFERERkZG5WoCydUDvkYDZqvVoKIpJkhSUtjWt3XDDDccUQyaTCfPnz8dLL72EwsJC5OXlweXyb71Uamqq2iZms9kAoE17XFce169fP6xatQoZGRnYsWMHBg0a5NdYbDYb8vPzO7z9eC137d1WUlKCcePGqZ8XFhaisbERR44cQWZmZqfn1Iuwt8x1FHe4aNEiFBUVYfTo0UhNTcXs2bP9+mYhIiKKaj+8BCw8D1j1jNYjIYoICQkJx1z39ddf47nnnsPWrVuxdu1a3HzzzX6fLzk5uUvjON7jBg8ejLvuugs9e/bEDTfcELR1Oe392493W58+fbB792718127diE+Ph4ZGRl+nVMvwl4QXXPNNaiqqlI/SkpKkJGRgdNOOw2XXHIJRo0ahbVr12LLli26n14jIiLS3OEt4rJih7bjIIpgdXV1AACHw4GVK1finnvu0eyN+927d+Obb77BypUrsWvXLsyfP9/vx7pcLlRXV7f56M6/Y/r06Xj99dfx/vvvY9u2bbj33ntx6623hmVfpWAKe0Fks9naLOZ67bXXcPnll2Pr1q1wOByYP38++vbti0cffRQLFy4M9/CIiIiMpe6wuGys1nQYRJHsggsuwKWXXoqTTjoJM2bMwC233IKysjIcOnQo7GPJz89Hjx49cNZZZ6GgoABxcXGYMWOGX48tKipCampqm4/9+/d3eSwnn3wyXn31Vfzud7/D6aefjlGjRmHu3LldPp9WJFnDvrSmpibk5eVhzZo1ePXVV7FmzRp1oZcsy0hPT0dlZWW7j3U6nXA6nernNTU1yM3NhcPh6PLUJBERkeEsOFusIco7Hbjpk07vThQMTU1N2LNnDwoKCtSIZQqPhQsX4s0338RLL72E+Ph4bNiwARdddBEOHz4cda+Bj/d9WFNTA7vd7ldtoGns9uLFizFmzBjk5+ejpqYGBQUF6m2SJMFsNne4c+/cuXNht9vVj9zc3HANm4iISD/qysVlY3h2uicibZ1zzjlobW3FkCFD0KtXL8ycORN///vfo64YCiZNo0Cef/55zJkzRwzEYkFMTEyb22NjY9HQ0IDU1NRjHvvAAw/gnnvuUT9XZoiIiIiihiwDdZ6WHRZERFGhoKAAX331ldbDiCiaFUQ7d+7Ezp07MWHCBABAWloaioqK2tyntrZWjRk8WkxMzDEFFBERUVRprALcLd5jIiIKmGYtc2+++SYuvvhiWK1iz4TRo0dj9erV6u3FxcVwOp1IS0vTaohERET6Vl/uPW5tAloatRsLEZFBaVYQffrppxg/frz6+bhx4+BwOPDaa68BAObNm4cJEybAbA7ezsNEREQRpe6ohCvOEhERBUyTgqixsRFr1qzBaaedpl5nsViwYMECzJgxAz169MDbb7+NefPmaTE8IiIiY1AitxUsiIiIAqbJGqK4uLg2kdmKKVOmYMeOHVi7di3Gjh2LzMxMDUZHRERkECyIiIi6TdOUufZkZ2cjOztb62EQERHpH1vmiIi6TdN9iIiIiKgbOENEFJBx48bhwQcfbHPdyJEj8Ze//MWvxxcXF0OSpIBvC+Q+/grmuY7n66+/hiRJbT4SExND/rzhxIKIiIjIqOo9BZHk+XPOgojouCZOnIjly5ernzscDmzcuBHnnXdet8/dp08fVFUF92fw7LPPxqJFi8L2fB1JTk5GVVWV+rF///6wPG+46K5ljoiIiPyktMylFgCVu4DGak2HQ1FOloGWBm2e2xoP+DFbMnHiRPz5z39GY2Mj4uLi8O2338Jut+Pkk0/u9hBMJhNSUlK6fR49Pp8kSWH9t4UbZ4iIiIiMSmmZyxwoLjlDRFpqaQAe7a3Nh5+F2Mknn4zExER178sVK1bgnHPOUbd5WbFiBUaMGIH4+HiMHj0aRUVFfv/zO2phW7JkCfr164f09PRjZns6er4ZM2ZAkiQsX74cN910EyRJwowZM/x6vqKiIpxxxhmw2+246KKLUFpaCkC0vuXn5+PDDz9EXl4eUlNT8dRTT/n97+uIMos1f/585OXl4cMPP/Trtm+++QYjRoxAamoqrrnmGlRXV/v1uFBgQURERGREbhdQf0QcZ54gLlkQER2XyWTCOeecg2+++QaAeFE+ceJEAIDb7caVV16Jq666Crt378bYsWMxe/bsbj3foUOHcPXVV+N3v/sd1qxZg08++US97XjP98QTT6Cqqgqnn346nnnmGVRVVeGJJ57o9Pnq6uowceJEnHfeedi4cSNyc3MxefJkuN1uAEBFRQXmzZuHjz/+GHPmzMHs2bPR2Nj5hs4OhwMpKSnqx2233dbm9hdeeAHLli3Diy++iLFjx3Z6W0lJCS666CLccccd+PHHH1FXV4dp06b5fc5gY8scERGRETVUArILgARk9BfXsSAiLVnjgQfLtHtuP5133nl444030NjYiB9//BGLFy9Wb9uwYQPsdjs2btyI2tpabN++vVvD+vTTT1FYWIhbbrkFADBnzhxcdNFFnT5fXFwc4uLiYLFYEB8f73e72kcffYSkpCQ8/PDDAICnnnoKmZmZ+P777wGIgum5557DkCFDMGDAAPz617/G4cOHkZeXd9zzJiUlYf369ernR4cq1NXV4ZtvvoHVaj3mse3d9u9//xtjx45Vvy7PP/88srOzcfDgQfTs2bPTcwYbCyIiIiIjUtYPxacDCZ59+1gQkZYkCbAlaD2KTk2cOBF33303vvnmG/Tp0wf5+fkAxOzR/Pnz8dJLL6GwsBB5eXlwuVzdeq4DBw4gNzdX/bywsFA9DsXzlZSUoKCgQP08JiYGvXv3RklJCTIzM5Gamorhw4cDAGw2GwBAluVOz2symdSvU3tmzJjRYeHS3m0lJSVtvha9e/dGTEwMSkpK1ILoeOcMNrbMERERGZGSMJfYA4hLFccMVSDqVH5+PnJycvDXv/5VbZcDxBqb5557Dlu3bsXatWtx8803d/u5srKyUFbmnTXbt29fQM9nMpn8KlgUffr0wZ49e9TPm5qaUFZWhj59+gAQaXGhkJDQcSHc3m19+vTB7t271c/3798Pp9OpjrOzcwYbCyIiIiIjUgIVEjN9CiLOEBH547zzzsNXX33VJm67rq4OgFgvs3LlStxzzz0BFSPtOf/887Ft2za8+uqr2LVrFx555JGAnq9fv3748ssvceDAAXz55ZedziBdfPHFqK2txZw5c7B37178+te/Rv/+/TF69Ohu/TtkWUZ1dXWbj+7MZl133XX47rvv8OKLL2LPnj2YOXMmpkyZgh49enRrnF3FgoiIiMiIlJY53xmi5lrA1aLdmIgMYuLEibBYLDjnnHPU6y644AJceumlOOmkkzBjxgzccsstKCsrw6FDh7r8PNnZ2Vi8eDHmzJmDM844A6effnpAz/eHP/wBe/fuRUFBAWbOnKmGI3QkMTERn332GT7//HMMHToU+/btwwcffACTqXsv+WtqapCamtrmY9WqVV0+X05ODj7++GM888wzGDlyJBISEvDKK690a4zdIcndLX11oqamBna7HQ6HI2TTgURERLrx2UPAqqeBsbOACXOAP6WJ63+7U8waEYVQU1MT9uzZg4KCAsTGxmo9HIpSx/s+DKQ24AwRERGRESktcwlZgMkMxNrF52ybIyIKCAsiIiIiI/JtmQO4joiIqItYEBERERlRfbm4TMwSlyyIiIi6hAURERGREakzRCyISDsRshSdDCpY338siIiIiIzG1QI0VIhjtsyRBpQNMxsaGjQeCUWz5uZmAIDZbO7WeSzBGAwRERGFUf0RcSmZgThPuhwLIgojs9mMlJQUHD4swj3i4+MhSZLGo6Jo4na7UV5ejvj4eFgs3StpWBAREREZjdIul5AJKPuLsCCiMOvZsycAqEURUbiZTCb06dOn28U4CyIiIiKjUSK3lfVDAAsiCjtJktCrVy9kZWWhpYUbAlP42Wy2bm86C7AgIiIiMp56pSDq4b2OBRFpxGw2d3sNB5GWGKpARERkNEcnzAEsiIiIuogFERERkdGwZY6IKGhYEBERERlNHVvmiIiChQURERGR0SgFUUKm9zqlIGpyAG5X+MdERGRQLIiIiIiMRl1D5DNDFJviOZBFUURERH5hQURERGQ07aXMWWyALVEcs22OiMhvLIiIiIiMpKXJOwOUmNn2NnUdUXVYh0REZGQsiIiIiIxEmR0y23za5DziPJ9zhoiIyG8siIiIiIzEN2FOktrexqQ5IqKAsSAiIiIykvYS5hQsiIiIAsaCiIiIyEjaS5hTsCAiIgoYCyIiIiIjUVvmso69TVlTxIKIiMhvLIiIiIiMpP44BRFniIiIAsaCiIiIyEjYMkdEFFQsiIiIiIzkeC1zLIiIiALGgoiIiMhI1JQ5FkRERMHAgoiIiMhIOENERBRULIiIiIiMwlkHtNSL487WEMly+MZFRGRgLIiIiIiMQkmYs8YDMYnH3q4URLILcNaGb1xERAbGgoiIiMgojtcuBwDWOMAcI47ZNkdE5BcWREREREZxvMhtAJAk7yxRU3VYhkREZHQsiIiIiIxCTZjL7Pg+DFYgIgoICyIiIiKjUFvmOpghAlgQEREFiAURERGRUXTWMgewICIiChALIiIiIqOoLxeXiWyZIyIKFhZERERERuHXDFGKuGRBRETkFxZERERERsE1REREQceCiIiIyAhkOcCUueqQD4mIKBKwICIiIjKCJgfgcorjjjZmBThDREQUIBZERERERqDMDsXYAWtcx/djQUREFBAWREREREZQr6wfOk67HMCCiIgoQCyIiIiIjMCfhDmABRERUYA0LYjuv/9+XHLJJernRUVFGD16NFJTUzF79mzIsqzh6IiIiHRETZg7zvohwFsQtTYBLY2hHRMRUQTQrCAqKirCs88+iyeffBIA4HQ6cckll2DUqFFYu3YttmzZgkWLFmk1PCIiIn1RE+Y6KYhikgDJLI45S0RE1ClNCiJZlnHbbbfh7rvvRt++fQEAS5cuhcPhwPz589G3b188+uijWLhwYYfncDqdqKmpafNBREQUsfydIZIkts0REQVAk4LoxRdfxPr161FQUIAlS5agpaUFGzZswJgxYxAfHw8AGDZsGLZs2dLhOebOnQu73a5+5Obmhmv4RERE4efvGiKABRERUQDCXhDV1dXh97//Pfr374/S0lLMnz8f48aNQ01NDQoKCtT7SZIEs9mMqqr2f5k/8MADcDgc6kdJSUm4/glEREThV+/nDBHAgoiIKABhL4jeffdd1NfXY9myZfjDH/6Azz//HNXV1Xj55ZcRExPT5r6xsbFoaGho9zwxMTFITk5u80FERBSx/G2ZA1gQEREFIOwFUWlpKU499VSkpaUBACwWC4YNG4ampiaUl5e3uW9tbS1sNlu4h0hERKQvbrdPQcSWOSKiYAp7QZSbm4vGxrYxoHv37sXf//53rF69Wr2uuLgYTqdTLZyIiIiiVmMVILvEcUInG7MCLIiIiAIQ9oJo0qRJ2Lp1K55//nmUlpbiqaeewvr16zFx4kQ4HA689tprAIB58+ZhwoQJMJvN4R4iERGRviiBCnFpgNna+f1ZEBER+c0S7idMS0vDp59+invvvRf33HMPevbsiTfeeAP9+vXDggULcM0112D27NlwuVxYvnx5uIdHRESkP4EkzAEsiIiIAhD2gggAxowZg5UrVx5z/ZQpU7Bjxw6sXbsWY8eORWamH20BREREka7es8Y20c+/iyyIiIj8pklBdDzZ2dnIzs7WehhERET6wRkiIqKQ0WRjViIiIgpAlwui6pAMh4gokrAgIiIi0rs6T8ucPwlzABCXIi45Q0RE1CkWRERERHrX1Rmi5jqgtTk0YyIiihAsiIiIiPRO3ZQ1y7/7x9oBSOK4qToUIyIiihgsiIiIiPSuPsCCyGT2FEVg2xwRUSdYEBEREemZqxWoPyKO/W2ZA5g0R0TkJxZEREREetZwBIAMSCYgPt3/x7EgIiLyCwsiIiIiPVPWD8VniFY4fzFpjojILyyIiIiI9EwNVAigXQ7gDBERkZ9YEBEREemZGrntZ6CCggUREZFfWBARERHpWaAJcwoWREREfmFBREREpGeB7kGkYEFEesNNgkmnWBARERHpmdoyxzVEZGA7vwIe7Q388JLWIyE6BgsiIiIiPVNmiBI4Q0QGtvNLwN0C7F6u9UiIjsGCiIiISM+63TJXHdThEHVJxU5xWV+u7TiI2sGCiIiISM/YMkeRQCmIlO9nIh1hQURERKRXrU6gqVocd3WGqMkBuF1BHRZRQFqbgaq94riOM0SkPyyIiIiI9EppLzJZgdiUwB6r3l8WRRGRVqr3ArKnKG+uBZobtB0P0VFYEBEREemV76aspgD/ZFtsgC1RHLNtjrRUsavt58reWkQ6wYKIiIhIr5T2ooTMrj2ewQqkB8r6IQXb5khnWBARERHpVVcDFRRxKeKSM0SkpaMLIs4Qkc6wICIiItKrrkZuK5g0R3qgFkSSuGDSHOkMCyIiIiK9qmdBRBFAWUPUY7C4ZMsc6QwLIiIiIr3qdsscCyLSmLMOqC0Tx31OE5ecISKdYUFERESkV2yZI6Or3C0u49KAzBPEMdcQkc6wICIiItIrpSBKYEFEBlXpaZdL7+dNS2TLHOkMCyIiIiK9UmeI2DJHBqUEKqT3834fs2WOdIYFERERkR41NwDNteKYLXNkVEqgQnpf7/dxPWeISF9YEBEREemRss7CEgvEJHXtHCyISGttZog8BVFzHdBcr92YiI7CgoiIiEiPfAMVJKlr52BBRFrzLYhsiYAlTnxex2AF0g8WRERERHrU3chtoG1BJMvdHxNRIBoqvcV4WqEo7BM9wQpsmyMdYUFERESkR91NmAO8BZHsApy13R8TUSCU2aHkbMAWL44ZrEA6xIKIiIhIj7q7BxEAWOPEGiSAbXMUfr6BCgqlwGfLHOkICyIiIiI9CkbLHMB1RKQd3/VDCrbMkQ6xICIiItIj5QWj8gKyq1gQkVbaLYjYMkf6w4KIiIhIjzhDREantsz5FEQJngKfLXOkIyyIiIiI9IgFERmZ2w1UtlMQcXNW0iEWRERERHojy0Cd5wVjQndb5lLEJQsiCqfaA0BLA2CyACl9vNezZY50iAURERGR3jhrgdZGcdydlDmAM0SkDWX9UEoeYLZ6r1db5jhDRPrBgoiIiEhvlPUVtiTAltC9c6kFUXX3zkMUiPba5QDvDFFLPeCsC++YiDrAgoiIiEhv6pU9iLrZLgdwhoi00V6gAgDEJAJWzyat9QxWIH1gQURERKQ3wQpUAFgQkTbUyO2+x97GtjnSGRZEREREeqO0zHV3/RDAgoi00d4eRAoGK5DOsCAiIiLSG6UgSmBBRAbkagGqisVxuwWREr3NljnSBxZEREREehOqljlZ7v75iDpTvQ9wt4q1Qkm9jr2dLXOkMyyIiIiI9CYULXMuJ9DS2P3zEXVGaZdLKwRM7bzUZMsc6QwLIiIiIr2pD2JBZEsUm2MCbJuj8FAT5toJVAC86Yn1nCEifWBBREREpDfBnCGSJK4jovA6XqACwBki0h0WRERERHoiyz4FURDWEAFAbIq4ZEFE4dBZQaSEhdQxVIH0gQURERGRnjRWAe4WcZwQhI1ZAc4QUXh1tCmrwrdljkEfpAMsiIiIiPREedc8NgWwxATnnCyIKFyaG4CaUnHc2QxRSwPQXBeecREdhyYF0axZsyBJkvrRr5/4gSkqKsLo0aORmpqK2bNnQ+a7BkREFG2CGbmtYEFE4VK5W1zGpQLxae3fJyYRsCaIY7bNkQ5oUhD9+OOP+Pjjj1FVVYWqqiqsW7cOTqcTl1xyCUaNGoW1a9diy5YtWLRokRbDIyIi0o6SvBWMQAWFUhA1VQfvnETtUSO3O0iYUzBpjnQk7AVRa2srioqKMG7cOKSkpCAlJQVJSUlYunQpHA4H5s+fj759++LRRx/FwoULwz08IiIibakzRCEoiDhDRKFW2cn6IQWT5khHwl4Qbdy4EbIsY8SIEYiLi8MFF1yAffv2YcOGDRgzZgzi4+MBAMOGDcOWLVs6PI/T6URNTU2bDyIiIsNjyxwZWWeBCgolMIQtc6QDYS+Itm7disGDB+M///kPtmzZAqvVittuuw01NTUoKChQ7ydJEsxmM6qq2v/lPXfuXNjtdvUjNzc3XP8EIiKi0KnztBAFK2EOYEFE4aNGbnfWMucp+NkyRzoQ9oLo2muvxerVqzF69GgUFBTg6aefxueffw63242YmLZpOrGxsWhoaGj3PA888AAcDof6UVJSEo7hExERhRZniMjIOtuDSKG0hLJljnTAovUAUlJS4Ha70bNnTxQVFbW5rba2Fjabrd3HxcTEHFNAERERGV6wN2UFfAqi6uCdk+hoDZVAQ4U4Tis8/n3VljnOEJH2wj5DdM899+DNN99UP//hhx9gMpkwdOhQrF69Wr2+uLgYTqcTaWkdRDYSERFFonqlIApmy1yKuOQMEYWSErmd1EtEax8PQxVIR8I+QzRixAg89NBD6NmzJ1pbWzFr1ixMmzYNEydOhMPhwGuvvYYbbrgB8+bNw4QJE2A2m8M9RCIiIm24XT6x2yGYIWquA1qbAUv73RdE3eJvuxzgbZmrZ6gCaS/sBdENN9yArVu3YvLkyUhKSsJll12GRx99FBaLBQsWLMA111yD2bNnw+VyYfny5eEeHhERkXYaKgDZDUAC4jOCd95YuzgnZLEXUTAjvYkUasJcJ4EKQNuWOVkGJCl04yLqhCZriObOnYu5c+cec/2UKVOwY8cOrF27FmPHjkVmZhDbBYiIiPROWT8Unw6Yg/gn2mQWRVFTtWibY0FEodCVGaLWRsBZC8Qmh25cRJ3QPFThaNnZ2cjOztZ6GEREROEXioQ5RVyqtyAiCoVACiJbAmBLFG2c9eUsiEhTYQ9VICIiog6oCXMhmMFh9DaFkiz7vymrQo3e5joi0hYLIiIiIr1QZ4hYEJHB1B4EWuoByQyk5Pn3mATuRUT6wIKIiIhIL9SEORZEZDBKu1xKH/9TDJVo+XruRUTaYkFERESkF6FeQwSwIKLQCGT9kIJ7EZFOsCAiIiLSC+WFYQJniMhgKgNcPwT4tMxxDRFpiwURERGRXtSxZY4MKpA9iBRsmSOdYEFERESkF2yZI6NiyxwZGAsiIiIiPXC1AI2V4pgzRGQkrlagco847lLLHGeISFssiIiIiPRAaRuSzEBcWvDPz4KIQsWxD3C3AJZYIDnb/8cphX/9YbGPEZFGWBARERHpge8eRKYQ/HlmQUShoqwfSisM7HtXKYhamwBnTfDHReQnFkRERER6oCRtJWSG5vxKQdTkANyu0DwHRaeuBCoAgDUOsCWJY7bNkYZYEBEREemBUhCFIlABAOJSvMdNjtA8B0WnrgQqKJRZIgYrkIYCLojy8/OxatWqUIyFiIgoeoUyYQ4AzFbvu/Fsm6NgCkZBVM+9iEg7ARdEp5xyCr799ttQjIWIiCh6qTNEIWqZA7iOiEKjogubsiqUFlG2zJGGAi6Inn32WSxZsgSPP/44GhsbQzEmIiKi6FMf4pY5wNs2x4KIgqWlEXCUiOMuzRBxLyLSniXQB2RlianNFStW4IEHHgAAyLIMSZLgcnGRJhERUZeoM0Qh2INIwRkiCrbKPQBkINYOxKcH/ni2zJEOBFwQ7dmzJxTjICIiim7KO+QJLIjIQJT1Q2l9AUkK/PGJ3JyVtBdwQZSXlxeKcRAREUU35QVhSFvmWBBRkFV2Y/0Q4H0DgC1zpKEuxW5/+OGHuOiiizB06FBMmjQJS5YsCfa4iIiIokdLI+D0RGGzZY6MpDsJc4BPyxxniEg7ARdEzz//PG655RaceeaZ+P3vf4+xY8fi5ptvxgsvvBCK8REREUU+Zf2Q2SbWYoQKCyIKtq5uyqrw3YdIloMzJqIABdwy9/jjj+Ott97CuHHj1OvOOOMMTJs2DbfddltQB0dERBQV6n3a5bqyDsNfLIgo2Lo7Q6S0zLmaxYbBvhsIE4VJwDNETU1NsNvbvntlt9vR1NQUtEERERFFFXVT1hC2ywEsiCi4Gqu9xXxXZ4issUBMsjhm2xxpJOCCaPr06bj66qvx3//+F99//z3eeOMNTJ06lbNDREREXRWOhDmABREFlxKokNgTiEnq+nkSGaxA2gq4Ze6RRx6B3W7Hww8/jJKSEuTl5eG2227Dr3/961CMj4iIKPKpCXMsiMhAurt+SJGQJVrv6rgXEWkj4ILIZDLh3nvvxb333huK8RAREUUftWUuhJHbQNuCyO0GTF0KmyUSglUQMWmONMbfhERERFoL2xqiFHEpu4Hm2tA+F0W+7gYqKNgyRxoLuCDKz8/HqlWrQjEWIiKi6FQfppY5axxgiRXHbJuj7gpWQaRuzsqWOdJGwAXRKaecgm+//TYUYyEiIopO4WqZA3za5qpD/1wUuWTZp2UuSDNEbJkjjQRcED377LNYsmQJHn/8cTQ2NoZiTERERNFFeWc8ITP0z8VgBQqGusOi7VIyAan53TsXW+ZIYwGHKmRliW/aFStW4IEHHgAAyLIMSZLgcrmCOzoiIqJI56wDWhrEcVhniFgQUTco7XIpfQBLTPfOpbbMcYaItBFwQbRnz55QjIOo+1qdwIENwL7VQMkaoPxn4PS7gZOu13pkRNQd+38EMgcCtgStRxIayrvi1gQgJjH0z8eCiIJBKYjSupkwB/i0zB0WrXiS1P1zEgUg4IIoLy8vFOMgClxDJVDyPVCyWhRB+38CXM6291n7MgsiIiPb/D7w1o3AqTOBC+dpPZrQUNrlEsPQLgd4k+ZYEFF3VAZp/RDgbRV1NQNN1d6inShMAi6IiDQhy0Dlbs/sz2pg3xrgyLZj7xefDuSeCqQWAKufEb+w+W4TkXFt/1Rc7v9R23GEUr1SEIWhXQ7gDBEFR7ACFQDAGgvE2oEmh2ibY0FEYRZwQZSfn4///Oc/OO2000IxHiKhtVm0vymzPyVr2k+fSe8P9DkVyB0D9BkjfjFLEtDcIAqiJof4ox+fFv5/AxF13z7PNg+OEm3HEUrqDFGII7cVTJmjYFAjt4PQMgeIdURNDtFCmjkgOOck8lPABZESu82CiIKqTfvbGqDsJ6C1qe19zDag90hR+OSOETNBCentn88WDyT1BmrLxLtYLIiIjKfmAFBVLI5rD4o3Siw2TYcUEsoaooRwF0ScIaIucrtE1wYQnBkiQLwhULHDO2NKFEYBF0TPPvssrrjiCsiyjFmzZiEuLi4U46JI19wAbPlAvPu7b3X77W9xaZ7i51Rx2WuEmFb3V3pfURBV7gZyRwdt6EQUJiWrfT6RgZpSIK1Qs+GETB1b5shgHCVivY85BrDnBOeciUyaI+0wdpu08cUfgR9ebHtdej9v65tv+1tXpRUCxSu8Cz+JyFj2rW77eXVJhBdEnCEig1AT5goBkzk450zgXkSkHcZukzYOrBeXQ64EhlzuaX/LCO5zKC+cKlgQERmSsn5IMgOyK3LXESkvAFkQkVGogQpBWj8EeFMW2TJHGmDsNmlD+WV6xt1Az6GheQ7lF7XS50xExuGsBQ5uEsf9zgV2fC5miCKREhijRcscUzipK0JSEHm+/+tYEFH4mfy501133QWn07u/y4oVK9Dc3Kx+XldXhwEDmAhCfmqsAhorxXEo21+UcyvR20RkHKU/ALIbSMkDck8R10XiDJEsazdD5HICLY3heU6KLGrCXJACFQCfljkWRBR+fhVEzzzzTJuCaPLkyTh48KD6ucvlwq5dbEsiP1V4ZmySeoV25/nUAnGpRG9TWw2VwCf3saWQ9ElZP9TnNMDeRxxX79NuPKHSVC0WpwPhS5mzJQImT4MIfzdSV4SiIFLeEGhviw2iEPOrIJKPenf96M+JAqKEHKQFcaq9PUr0NsAX/e1Z9Qzw/QvAsr9oPRKiYynrh/qM8aZYOUq1G0+oKIlaMfbAUjS7Q5K4joi6rtXpfXMiFAVR3WF2dVDY+VUQSZIEyafHWGK/MXWH2nschrQoriPqmBJssf9HTYdBdAxXC1C6Vhz3OQ1IyRXHNfsBt1u7cYVCuNvlFCyIqKsq9wCQgZhkICEzeOdVzuVu4fclhZ1foQqyLOP+++9HTEwMAKChoQF/+tOfkJycDABoamo63sOJ2grXDBHA6O2OyDJQtl4cV+8F6is63uSWKNwObARaGsSL9owBYi2RZBatZXWHgOReWo8weFgQkdGo7XJ9gxvIYYkBYlNEG2l9OTdUp7Dya4boxhtvRENDA6qqqlBVVYVf/vKXcLlc6ueNjY244YYbQj1WihShSKfpCKO321d7AGg44v38wDrtxkJ0NKVdLncMYDIBZguQ7Gl/jbRgBTVhjgURGYS6B1EI/oYnci8i0oZfM0SvvPJKqMdB0UKWwztDxJa59h3Y0Pbz/euAfhO0GQvR0XzXDynsuaIYqt7nTZ2LBOoMUZgitxUsiKirlL/hwVw/pEjIAo5sZ9IchZ1fM0REQdNQKVLfACCtIPTPx+jt9h3YKC6VpKmyn7QbC5EvWW6bMKdQ1hFF2gyR8sIvmGsx/MGCiLqqIoQFUSKjt0kbLIgovJSp9uQcwBoX+udj9Hb7lBmigZPE5X4WRKQTFbtEO6c5Bug9wnu93VMQRdrmrMoLP84QkVH4riEKNjV6mwURhRcLIgqvyjAmzAGM3u6IUhCNvAGQTEDdQaDmgLZjIgK87XLZo8Qia0XEzhCxZY4MpKnG+z0byoKojnsRUXixIKLwqgjj+iEF1xG1VV8B1Hj2c8k9BcgcKI7ZNkd6oLTL5Z3W9vqInyFiyxwZgPKmZkIWEGsP/vkTGKpA2mBBROFVGcaEOYXvOiICDnpmh9L6ArHJQO+TxOdsmyM9UAMVjiqIUvqIS0dJ5KwHdLt9UubCPUOUIi4bq8P7vGRsoVw/BLBljjTDgojCS4sZIkZvt6W0y/UaLi6zR4rLMkZvk8ZqD3neuJCAnNFtb7PniMvmusiZ1WisBGSXOGaoAhmBun4oRG3vbJkjjWheEF1wwQVYtGgRAKCoqAijR49GamoqZs+eDTlS3gUkQZa9bWuhenepPWyZa+vogkiZISr7KXLeeSdjKvG0y/UY7J3BUFjjgPgMcewoDeuwQkZpC4pLA8zW8D43CyLqilDPECX4zBC53aF5DqJ2+LUP0a9+9Su/Tvbyyy8H9OSvv/46PvvsM0ydOhVOpxOXXHIJzj//fLzxxhu46667sGjRItx0000BnZN0rO6weHdXMgGp+eF73qOjt4O5s7YRqQXRMHHZYzBgsooXRlXF4YlDJ2qPGrc9pv3bU3JFAp2jxPv9a2RaJcwB3oKopR5odbYNsCDqiDpDFKqCyDNT6m4FmqqB+LTQPA/RUfyaIcrLy1M/ZFnG4sWL0dDQgJ49e6K5uRlvvPEGnE5nQE9cWVmJe++9FyeccAIAYOnSpXA4HJg/fz769u2LRx99FAsXLuzw8U6nEzU1NW0+SOeUNTz2XMBiC9/zMnrbq6nGO1PW0zNDZIkBeg4Rx2ybIy11tH5IEWnBCmpBlBX+546xA/C8OcR1ROQPWQ79DJHF5i3WGaxAYeTXDNHDDz+sHo8aNQpvv/02Lr74YvW6Tz/9FH/4wx8CeuJ7770Xl112GRobGwEAGzZswJgxYxAfHw8AGDZsGLZs2dLh4+fOnYs5c+YE9JyksQoNAhUAb/R2bZkYQzS/43Rwk7i05wIJ6d7re58kiqGyn4Ahl2szNopuzjrvhsEdzhD5BCtEAjVyW4OCyGQSbYmNVeIjSYNZKjKW+iOA0wFA8r7RGAoJWeJ7su4wkHVi6J6HyEfAa4j27NmD/Pz8Ntfl5ORg927/12f873//w1dffYXHHntMva6mpgYFBd4fMEmSYDabUVXV/jv6DzzwABwOh/pRUhIhfyAjWaUGgQoKriMSjl4/pOjtCVbYzxki0sj+tSJgwJ7rDVA4mjpDtC984wqleg1b5gCuI6LAKO1yKbmANTZ0z6MmzTFYgcLHrxkiX9dddx0mT56MO++8E7m5uSgrK8MzzzyDX/7yl349vqmpCbfddhuee+45JCcnewdisSAmpm0Pc2xsLBoaGpCamnrMeWJiYo65P+mcVjNEgFhHVLyC0dtKQdTzqPUX2Z5ghQPrAbcLMJnDOiyiTtcPAZG3OauWLXMACyIKjFIQhfpNzUTuRUThF3BBNH/+fPTr1w/vv/8+Dhw4gKysLNx2222YNWuWX4//85//jNGjR2PSpEltrk9LS0NRUVGb62pra2GzhXGtCYWWMjujxQwRo7eFg56WpKNniDJOAKzxIvSiYieQeUL4x0bRTV0/dJyCKOLWEHle8CWwICIDCHWggkLdnJV7EVH4BFwQWSwW3HXXXbjrrru69ISLFy9GeXk5UlJSAAANDQ148803kZ+fj5aWFvV+xcXFcDqdSEuL4vUekaRN5DZb5jTR3ACU/yyOjy6IzBYxa1SyWmzQyoKIwsnVCpT8II77jO34fsoMUcMR8f1siw/92EJJ2WuFM0RkBJUhDlRQsGWONNClfYgWL16MqVOn4vTTT8eOHTvwi1/8AkeOHPHrsStWrEBRURHWr1+P9evX49JLL8Wf/vQnfPPNN3A4HHjttdcAAPPmzcOECRNgNrN1JyLUHgBaGgDJ7F0YHU5HR29Ho8NbANkt3n1L6nns7dk++xERhdOhTSL+OdYOZA7s+H6xKYAtSRxHwl5EaqgC1xCRAYQ6YU7BljnSQMAF0UMPPYT7778fhYWF2LBhA0wmcYrbbrvNr8fn5OQgPz9f/UhMTERGRgYyMjKwYMECzJgxAz169MDbb7+NefPmBTo80ivlF2lqXvg3IAQYvQ2I9UGA2L+lvb2Y1A1aGaxAYbbX0y6XO0akn3VEknzWERk8WMHVCjRUiGPOEJHeud3hWwfMljnSQMAtcy+++CK+/vprDBo0CM899xysViseffRRjBo1qksDWLRokXo8ZcoU7NixA2vXrsXYsWORmZnZpXOSDmmZMAcwehvwRhof3S6nUGaIDm4CXC3aFK4UnfxZP6Sw54jZTqOvI2o4AkAWG1XHp3d695BgQUT+qikFXE6xiXeouzzYMkcaCHiGKCUlBfv2tX1nrqKiAj16BGfKPzs7G5MnT2YxFGm0TJhTRPs6oo4itxWpBWKzxtYm8YKTKBxk2SdhroMNWX0pwQpGb5lTAxUytUt1ZEFE/lIT5gpD//2a6DND5HaH9rmIPAIuiH7/+99jypQpuOaaa+B0OvHkk0/immuuCXhjVooyWibMKXzXEUWb1mZvkXN05LbCZAJ6jxDHbJujcKncLfbjMdu8+2EdT6REbyvtQFolzAEsiMh/4XxTM8Hzhrjs4vcmhU3ABdENN9yAL774AgkJCTj77LNRV1eHV199Fddff30oxkeRQv1lWqjdGKI5erv8Z8DVLGaAUvM7vp/SNrefwQoUJsrsUO+T/NvsMVKit7XegwjwFkRN1dqNgYxBjdwOQ0FktgJxnrZ2BitQmAS8hggAzjzzTJx55pnBHgtFKrcbqNojjrWcIYrmljl1/6EOAhUUyjv0TJqjcAlk/RDgXb9g+BkijRPmAJHaB/BdeOpcuBLmFIlZQGOlmD3GoPA8J0W1gGeINm3ahIaGhlCMhSJVzX6xLsVk9b67q4Vojt7ubP2QQkmaO7wVaGkM7ZiIgMDWDwHe3yE1ZSKpzajUGSIN18uqM0QOwO3Sbhykf+HalFWhriNisAKFR8AF0fnnn48ffvghFGOhSKWs2UnNFxuAaiWao7f9LYjsOaJ/290KHCwK/bgoutUfASp2iOPcU/x7TGIPsd5IdonUSKOqVwoiDWeI4lK8x00OzYZBOtfaDFTvFcfhKogSuBcRhVfABdG0adPw6quvhmIsFKn0kDAHeKO3gehaR+R2iShtoPOCSJLYNkfho8wOZQ3yPwrfZAKSs8WxkdcR1emgIDJbvRvdRtubROS/qmKxqbctMXzfr2r0NvciovAIuCA699xz8fPPP2PSpElYunQpvvnmG/WDqF16SJhTROM6oopdQEsDYI3379293gxWoDAJdP2QIhKS5nxjt7XEpDnqjG+gwvHWoAYTW+YozALuX5o+fToA4MCBA7j99tvV6yVJwu7dUfQik/ynh4Q5RVoBULwiuqK3lXa5nkP92z9CSZpj9DaFmloQ+bl+SGH3BCtwhqj74lIAxz4WRNQxdQ+iML6pyZY5CrOAC6I9e/aEYhwUyZTiQw8zRMoYoqll7sB6cdnR/kNHU1rmjmwHnLVATFJIhkVRrrneW6wHOkNkzxGXjn3Hv59etTq9Uddaxm4DnCGizoU7UAFgyxyFXcAtc0QBcbuASk8RrfUaIt8xRFPLnL+BCorELCA5B4AMlK0P1ago2u3/UYR3JGcHnj6ptsyVBn9c4aDMDpms3oJEKyyIqDPK30stCqI6FkQUHgHPENXX1+PZZ5/F9u3b4XKJmE5ZlrF+/XqsW8cWGzpK9T7A3QKYYzwvsjV2dPR2uPqhtSLLPnsQ+VkQAUD2SKCmVLTNFXDPMQoBNW57TOA/h0bfnLXeZ1NWrX8HsSCizmgxQ6S0zNUfEXsZmvj+PYVWwN9h119/Pd5//31s3rwZu3btQmZmJt555x2MHz8+FOMjo1Pb5Qr08Qst2qK3q/eKf6vZBmQO9P9xSrACk+YoVLq6fghoO0NkxD3F6nwKIq2xIKLjcdYBtQfEcTjXASdkAJBEvH5jZfiel6JWwK9Qv/rqK7z11luYM2cOrFYrHnvsMTz//PPYvHlzKMZHRleho4Q5IPqit5V2uawTAYvN/8cp64iYNEeh4GoFSr4Xx4GuHwI8s80S0Noo3kE2GjVhjgUR6ZzypmZ8RnjbO81WbxQ/gxUoDAIuiOx2O7Zt24ZTTz0V69atg8vlwvjx47Fq1apQjI+MrlJHCXOKaFpHFOj6IYVSEFXvBRr47hwF2eHNQHMdEJMs9iAKlMUGJPUUx0YMVlCihDlDRHqnRbucIoHriCh8Ai6IHnzwQZx//vlobW3FmWeeibPPPhtXXXUVhgwZEorxkdFV6ChhTpHmaZuLhujtA11YPwSIKF7l/4xtcxRsyvqh3FP9i4Jvj5HXESnveGsduQ2wIKLj03JjdTVpjnsRUegFXBDNmDEDP/30ExISErBo0SKce+65OO200/Dmm2+GYnxkdJUa/jLtSLREb8uyN3K714jAH6+2zTEshYKsqxuy+jLy5qxqQcQZItI5301Zwy2RexFR+AScMgdAnQ2KiYnBI488EszxUCRxtQBVe8WxFtPtHYmWlrnag+KdNcnUtbak7JOAorc5Q0TBJcvA3m4EKiiMPENUz5Y5Mgh1hogtcxTZAi6ICgoKIHUQE7p7d4S/wKTAVO8TCTHWeCCpl9aj8YqW6G1l/VDGCSJMIlBq0hxniCiIqoqBuoNiD57sk7p+noiYIdJZyxzjjcmXLAMVO8SxFgUR9yKiMAq4IFq0aJF63NDQgB9++AEvvfQS5s2bF8xxUSRQ1w8V6qvoODp6W0myiTRd2X/IV69hYnap9gBQcwBI1lFRS8alrB/qPRKwxnX9PPY+4tKIM0TKCzxdpMyliEvZDTTXArF2TYdDOtJQKf5OAt43EsNJXUPEgohCL+CC6Kyzzmrz+YUXXoipU6fitttuwzXXXBO0gVEEqPQpiPREid6uLRNFW6QWRF1NmFPYEsTeRYe3iLa55EnBGxtFr2CsHwIAu2ejZ6OlzDXXi4Q9QB8tc9Y4wBInIswbq1gQkZeyfsie2703L7pKnSFiqAKFXlDmxvPz81FcXByMU1Ek0TKdpjPRsI5ILYiGdf0cbJujYFNmiLqzfgjwtsw1OYCmmu6dK5yU2SFLHBCTpO1YFFxHRO3RMlAB8FlDxFAFCr2AZ4huuummNmuIXC4XfvjhB5xwwglBHRhFgEodRm4r0gqA4hWRG73dUOldW9FzaNfPkz0SWP9vbtBKwVFfARzZJo5zT+3euWKSgNgUoKkacJQCsV0IDtGCUhAlZuqnlTguVcyYsyAiX0pBpNXfcGWGqOEI4HZ1PaKfyA8BF0T5+fltPpckCWeddRamTp0arDFRpNDzDFGkR28rs0Nphd1rgVGit8t+iuwACgqPkjXiMuMEICG9++dLyQUOVoviv4dBCiJlPYQeAhUUnCGi9lRqmDAHAPEZACSxvq2hUryJQBQiARdEDz/8cCjGQZGmtdk7Q6HHGaJIb5nr7vohRY8hIg2ssQqo3guk5nd7aBTFgrV+SGHvAxzcJBItjUJPCXMKJViBBRH50jJyGwDMFiA+XcwQ1R1iQUQhFXBBNH78+A5jtxXLli3r8oAoQlQVi3d1bIn6WDh8tEiP3lYKop7dWD8EAJYYoOcQsYZo/08siKh7lPVDeWODcz4jRm+rCXM6enHHGSI6mtutjy6PxCxREDFpjkIs4FCF/v37o6ioCKeddhqmTp2K0aNHY9OmTRgyZAhuvPFG3HjjjaEYJxlNpU4jtxVHR29HmmDNEAFt2+aIuqql0RvOEbQZIgNuzlqn55a5ak2HQTpSWyaSB00WICVPu3FwLyIKk4BniFauXImPPvoIp57qXRA7ZcoU3HrrrXjqqaeCOjgyMD28s3Q8kRy93VTjLUiDUhCdBOBlYD+T5qgb9v8IuFvEJs3BeoFl5BkiPc2cc4aIjqYEKqQWiNY1rSSwIKLwCHiGqLy8HG63u811brcbhw/zm5V86DlhThGp64gOFYnL5BwgIaP758v2RG8fWC/aKIi6wnf9ULBmjQ05Q6SsIWJBRDqmRm5rtH5Iwc1ZKUwCLvtnzpyJyZMn47rrrkPv3r1RUlKCxYsX46677grF+Mio9D5DBERu9HYw9h/ylXECYI0Xm0lW7AAyGbFPXRCs/Yd8pfQRl3UHgVanWPOmd0yZIyPQy99wtsxRmAQ8QzRnzhw899xzqKiowFdffYXa2losXLgQf/jDH0IxPjIqZdZFzzNEkRq9Hcz1Q4Bol1DCGbgfEXWF2wWUfC+Og7V+CBAJVJY4cewoDd55Q0WW2TJHxqCXgogtcxQmXWoMveKKK3DFFVegpaUFTU1NSErSyW7bpA8tTd4XJ1r/Mj2eSG2ZO7BRXAarIAJE21zJarEofsQvg3deig6HtwDOGsCWBGQNDt55JQmw54iZS0eJvn/fAOJr0NokjhNYEJGO6a5lrlzbcVDE82uGqLa2Fvfeey/uvPNO9bqnn34aKSkpSElJwRlnnIF9+wy0DwSFVtUeADIQYxfv4OrV0dHbkaClESj/WRwHsyDq7VlHxKQ56gqlXS53dPAXaKvBCgaYIarzvKizJYlgF73wLYgi5XchdZ2rRWydAeinIFLW3hGFiF8F0c0334xVq1Zh8uTJAIBPPvkE99xzD5599lkUFxfjhBNOwO233x7SgZKBqFPtOo3cVkRi9PahLYDsEjt8J/UK3nmV6O2Dm8QfS6JAqIEKQVw/pDBSsIIeAxUAb0HkagZaGrQdC2mvaq/4O2KND+7fka5QZlIbKkTrLVGI+PVW3RdffIF169YhPz8fAHD//ffj9ttvV/cceuSRRzB06NCQDZIMxggJc0BkRm8fWC8uew0PbjGaVihm/JwO4PDW4AU2UOSTZWCvT8JcsBkpeluvBZEtATBZRSx6Y5X4nKKX2i7XV/s3NRMyAMkkNnpvqNDfzw5FDL9miHr27Ilt27YBABYuXIidO3fiwQcfVG//+eefkZKSEpIBkgHpZTGmPyJtHdHBEKwfAgCTCeg9QhyzbY4C4SgRbzqYLED2ycE/v92TNFdtgLZtZR2E3l7USRLXEZGXXtYPAYDJ7G29Z9schZBfBdHf/vY3XH311Rg4cCBmzJiBv/71r8jKykJpaSl+9atf4corr8Tdd98d4qGSYRghYU6R5mmbi5To7WAnzPlS9iNi0hwFQpkd6jUiNOtmDDlDpKPIbQULIlIoBZFe/oYrPy9MmqMQ8qtlbtKkSdi8eTPWrFmDfv36Ydgw0S7jcrmQnJyMN954AxdeeGFIB0oG4jvdrneRFL3tagEObRbHoWhpU9YRla0L/rkpcu0LYbsc4F1D5NgvNg42BbybRPgoBZGeEuYULIhIobxBqIcZIgBIyBSXLIgohPyO+8nOzsbll1/e5rq8vDw8+eSTwR4TGVlzPVB7QBwrKW56pibNRUDLXPnPYlF0jN0bGBFMStLc4S0iWt0aG/znoMgTig1ZfSX1AiSzWP9SdxBI7h2a5wkGPe5BpGBBRIoKnRVEavQ2CyIKHR2/lUaGpBQWcanGCClQ1xBFQPS2uv/QsNAshLXniHfq3K0ibY6oMw2VQPlWcRyqGSKzBUjOFsd6T5pTCyK2zJFONdcDNfvFsV66PBK5OSuFHgsiCq4KgyTMKSIpejuU64cAUWSpbXNcR0R+KPleXKb3F2lRoWLPEZd6X0ekFkSZ2o6jPSyICPB5UzNNP29qJrAgotBjQUTBVWmghDnAG70NGH8dkVIQ9QxhJLa6QSvXEZEfQr1+SKEEK+g5ac7t9rb86HqGqFrTYZDG9JQwp2DLHIUBCyIKrgoDJcwpIiF62+3ytrGFaoYIYNIcBSbU64cUarBCaWifpzuaqkW7KeBdJK4ncSnikjNE0U3PBVFdubbjoIjGgoiCy2gzREBkRG9X7gZa6gFLHJDRP3TPo7TMHdkOOGtD9zxkfC1N3tbKcM0Q6bllTkmYi00BLDGaDqVdbJkjwCdQQUehSGrLHPchotBhQUTBZaRNWRWREL2ttssNFRvZhUpiFpCcA0D2PidRe8rWidTDhKzQJ04qM0R6DlXQ8x5EgM8MUbWWoyCt6S1hDvD+zDRUAK5WbcdCEYsFEQVPU423x9dILXOREL19YL24DMX+Q0fL9swSsW2OjkdZP5R3WmhSD32l9BGXjhL9pkUq7T56jNwGOENEgh5b5uLTAMkEQAYajmg9GopQARdE7777LvLz82E2m9UPk8kEszmE70qTMSgFRUImEJus7VgCEQnR26FOmPOlBiuwIKLjUAMVQrx+CPCmzDXX6fcFvTpDxIKIdKqhEmisFMd62kfQZAbiPSmVTJqjEPF7Y1bFzJkzMW3aNEyfPh02my0UYyKjqjRY5Lbi6OhtvUSN+kuWffYgCkdBpERvM2mOOuB2A/vWiONQrx8CAGuceCOmvlzMEunxZ1j3LXOegqilHmh16nOdE4WW0i6XnA3YErQdy9ESe4gOFCbNUYgEXBBJkoRbb70Vffsa7EUvhZ6SMGek9UOAN3q7tkz8QdDji6njqd4nEqxMViDzxNA/n1IQVRWLdxSN9vWi0CvfCjgdgDUB6DE0PM9pzxUFUXVJeN4YCFS9zlvmYuwAJACyWEeUpNPCjUJHbZfT4d/wxEzgEDhDRCETcMvcX/7yF8yaNQtHjrCPk46izhDpaKrdX0aO3lba5bJOBCxhmLWNS/HOArJtjtqjtMvljgbMAb/v1jV6T5pTZogSdFoQmUyM3o52elw/pODmrBRiAf+lev3117F582bk5eXhxBNPRHKyd63IsmXLgjo4MhgjJswp0gqA4hXGjN4O5/ohRe+R4mu1fx3Qb0L4npeMIVz7D/nSe9JcnY43ZVXEpYpiiAVRdFIKIj22vaubs3IvIgqNgAuiadOmBeWJKyoqsG3bNgwYMAAZGRlBOSdpzKhriABjR28fDOP6IUX2SUDR21xHRO1TC6IwrB9SqJuz7gvfcwZCLYh0OkMEMFgh2h3eIi4zBmg7jvYkci8iCq2AC6Ibb7yx3eubm5v9Pscbb7yBmTNnIj8/H9u2bcPLL7+MqVOnoqioCDfddBN27tyJ6dOn4/HHH4cU6rhWCo7GarFHAGDMljkjR2+rM0QjwvecTJqjjlSXiLY1yQxknxy+503R8QyR2+WNC2ZBRHrUWC023AbEG156o8yssmWOQiTgNUQHDhzAHXfcgfPOOw/nnHMOzjnnHIwfPx55eXl+Pb66uhqzZs3CihUrsG7dOrzwwgv43e9+B6fTiUsuuQSjRo3C2rVrsWXLFixatCjQ4ZFWlNmhxJ5ATKK2Y+kKo0Zv1x4U75hJJqDH4PA9b69h4jlrDwA1B8L3vKR/JZ50uV7Dwvu7QJ0hKg3fc/qr/ggguwFI3vhgPWJBFL2UN7dS84EEHX6PJmSKSxZEFCIBF0TXXXcdDh06hLi4OMTFxeGqq67Ctm3bMHPmTL8eX1tbiyeffBJDhgwBAAwfPhxVVVVYunQpHA4H5s+fj759++LRRx/FwoULAx0eacWoCXOKo6O3jUKZHcoYINLywsWWAGQOFMdsmyNf4dx/yJcyQ9RwBGhuCO9zd0aJCk7ICF/IRFewIIpepT+Ky3DO6gZCXUPEgohCI+CC6Pvvv8czzzyD3/72t3A4HJg5cyYWLlyITz/91K/H5+bm4tprrwUAtLS04G9/+xsuv/xybNiwAWPGjEF8vHhRN2zYMGzZsqXD8zidTtTU1LT5IA0ZOWEO8EZvA8ZaR6RFoIKCbXPUnr0aFUSxKYAtSRzrbZZI7wlzChZE0Wv/WnGZPUrbcXREaZlrqARcLdqOhSJSwAVR79698eWXX2L06NHYvHkzGhsbMWTIEGzatCmg82zYsAE9evTA559/jieffBI1NTUoKChQb5ckCWazGVVV7f9injt3Lux2u/qRm5sb6D+FgsnICXMKI0Zva1kQZXv2I9rPgog8Gqu8C7PDGagAAJLkE72ts2AFIwQqACyIopUsA/s9M0Q5Op0hiksT6xIhixZUoiALuCCaO3cupk+fjrq6OkyZMgVDhw7F+PHjcfrppwd0nmHDhuGrr77C4MGDcdNNN8FisSAmpu3O2LGxsWhoaL/14YEHHoDD4VA/Skp0uJA2mhg5YU6R5inIjRS9fcCTMNdzWPifW9mgtWydsdZdUeiU/ABAFr8HtHjxr9fobSNEbgMsiKJV9T4RZ22yavO3xB8mk3dtE9vmKAQCbma+/PLLUVZWhqSkJCxYsACvv/466uvrccMNNwR0HkmSMHLkSCxatAh5eXmYO3cuioqK2tyntrYWNlv7G03GxMQcU0CRhiJhhsho0dsNld53wnsODf/z9xgi/oA2VgLVe8ViXIpuWq0fUuh1c1a1IMrUdhydYUEUnZR2uZ5DAGustmM5nsQs0X5ax72IKPgCniECgNTUVFgsFsiyjBtvvBEzZ85EUlKSX49dtmwZZs+erX5usYiabODAgVi9erV6fXFxMZxOJ9LS0royRAqnhkqgqVocpxYc9666ZrTobaVdLrXAu8N8OFlixB9QgG1zJGix/5Av3c4QedYQcYaI9EjvgQqKBO5FRKETcEFUW1uLW2+9FT169EB8fDw2bdqEnJwc/Pjjj349fuDAgXjhhRewYMEClJSU4P7778fEiRMxadIkOBwOvPbaawCAefPmYcKECTCbzYEOkcJNmVFJzg5v0lmwGS16W4sNWY/m2zZH0a3V6V2HwBmituqN1jJXrekwKMz0HqigUH5+2DJHIRBwQXTTTTehtLQUr732GhISEmC32zFr1izccccdfj2+d+/eeOutt/Dkk09i8ODBaGhowL/+9S9YLBYsWLAAM2bMQI8ePfD2229j3rx5Af+DSANGT5hTGC16Ww1U0LDnW02aY0EU9crWAy6n2GdHq9ZZvc4QVXtaW41SEDkdgKtV27FQeLhavH9L9BqooEjkXkQUOgGvIfryyy9RVFSEnJwcmEwmSJKE66+/Ho8++qjf5zj//PPbjdSeMmUKduzYgbVr12Ls2LHIzNR5vzUJkbB+CPBGb9eWiX9TvM7bNbVMmFMoO5qXrQfcbrHwlaKTun5ojEh804JSENWWiRd6Zqs24/BVfwSoKhbHWv6s+iM2xXvc5AAS0jUbCoXJoc1AaxMQa9d/KJLaMseCiIIv4FcvAwcOxKuvvgpABCNIkoRVq1Zh8ODBQRlQdnY2Jk+ezGLISCp2iku9/zL1h1Git5213q97Tw1fZGWcAFjigOZaoGKHduMg7anrhzRqlwPEDIzZBshuoKZMu3H4KvleXGYO1GatXyDMFiAmWRwbYZacus+3XU7vb2ixZY5CKODv/n/+85948sknkZ2djdraWlx99dW4++678cwzz4RifGQElREyQwQYJ3r7oCeRMTlb2+Qqs8X7rjfb5qKX2w2U6KAgMpnEzwSgn81ZSz0FUc5obcfhL6VoY0EUHYwSqACwZY5CKuCWudGjR2Pnzp1YsmQJ9u/fj5ycHEyaNAl2uz0U4yO9k2WgwjObEgkzREaJ3lba5fSwZ0T2SeLF8P6fgOFTtR4NaeHINvEC2hqv7Zo2QAQrVO3RT7BCyQ/iMvdUbcfhr7hUseaJBVF0MEqgAsCWOQopvwoiJfntaD179kRrays++OADAAh4LyKKAPXlol0KUmTsQ2OU6G09rB9SqMEKjN6OWsr6oZyTtV+3Y+8jLvUQrOBq8Sbv5Z6i7Vj8xejt6NHkAI5sF8d6D1QAvC1zjZX6WSNIEcOvgmjatGmQJAlmsxknnXQS4uO90cqyJ55YkiQWRNFImUmx5+p7Qzd/HR29rdXi8M7oqiDyRG8f3MQ/UtFKD+uHFGr09j5txwEAh4qA1kaxYD29v9aj8Q8Louih7B+XkgckZGg7Fn/EpQKSGZBd4s3Y5N5aj4giiF8F0dNPP40tW7agqKgIP//8M1JSUjBs2DAMHz4cw4YNw7Bhw5CXlxfqsZIeqeuHDB65rTg6eluPSXMtTUD5z+JYDwVRWiEQYxdRvYe3at8yReHnmzCnNT1Fbyvtcjmn6H/BuoIFUfRQ2uWMMDsEiJ+hxCyg9oBom2NBREHkV0F0++23t/n8wIED+Oijj/Dmm2/ij3/8IwAgOTkZVVX8BRp1lBmiSFg/BBgjevvwZvEOWXy6Pv4gmExA7xHAnuWibY4FUXRx7BdrTiSTPoID9LQ5a8kacWmUdjmABVE0MVKggiIh01sQEQWRXwXRJ598gs2bN6sfxcXFKCgowLBhwzB//nx1toiiUCQlzCnS+4qCqHI3kKuDF3hH822X00tLX++RnoJoHTBqmtajoXBS0uV6DgVikrQdC+CdIXKUat/2arSEOYAFUbSQZWMFKigSPcEKjN6mIPOrILr44oshSRJMJhNOPvlkXHjhhTCbzXC73diwYQM2bNiAf/3rX3j55ZdDPV7Sm0hKmFOkFQDFK/Qbva2n9UMKZYPW/QxWiDp6Wj8EeGK3JbHZZH259wVUuNUe9M6cGekFJwui6FC9T/x8mCzGmtVXghU4Q0RB5ldB9Mc//hGSXt6JJv2QZW8aW3o/bccSTHqP3j6wUVzqqSBSkuYObxFrnCIhYIP8s+t/4jJvrLbjUFhsQFJP0VZTXaJdQaRsyJo1CIhN1mYMXcGCKDoo6Yc9hgDWOG3HEogE7kVEoeFXQfTII4+EeBhkSLUHgZZ6kfqSGkGhGnqO3na1AIc2i2M97EGksOcA8RlAwxGRNqfHVkMKvopdQMUO8S5z4XitR+NlzxUFkWMfkKPR7IwR2+UAFkTRQimIjBKooGDLHIWIQWJvSJeUlrKUPpEVtXx09LaelG8DXE4gJtmbiKcHkuRtmytbF57nbG0GPpkN/OsyoLk+PM9JbW3/TFzmna6vWZAUHSTNGW1DVgULouhQqqwfMlpBxJY5Cg0WRNR1FREYqAAcG72tJ8r6oZ7D9BfjG84NWhurgH9fDny/ANi1DNj8fuifk461/VNxOeACbcdxNN9gBS20NnvfGDBSwhzgLYiaqgG3W9OhUIi4WoAD68Wxkda3AWyZo5DR2SsqMpTKCIvcVijR24D+1hEd1OH6IUW4ghWq9wELzxfBF4pNb4X2OelYTTXA3pXieMD52o7laFpHbx/cKGZy49O9LbhGEZsiLmU34KzRdCgUIoc2i9CRGLvx1v8qM0RsmaMgY0FEXRepM0SAT9ucztYRqQlzOlo/pOg9Ulwe2Q44a0PzHGXrgJcmAEe2iaL16tfF9XuWA7WHQvOc1L5dywB3K5DeX3+/A+x9xKVWLXNKoELOKfqJxveXNRawxotjvc2QU3Ao64eyT9Jfp0FnlDVEjVViJpYoSAz2k0C6UhmBkduKNE/bnJ6it91ufSbMKRKzgOQcALK3cAum7Z8Br0wC6g6JZKTpXwInXiwWrctuYPN7wX9O6piyfkhvs0OAzwzRPm2eX92Q1WCBCgquI4psRg1UAMQMpsmTB1ZfrulQKLKwIKKucbt9IrcN1hLiDz1Gb1fuEql+ljjxrrweZXtmiYLdNvfDQuA/U8W/v3A8cNNSwJ4tbht6lbhk21z4uF3Ajs/FsR4LImUNUZNDtPaFW6knUCHHYOuHFL7riCjyGDVQARAzWglMmqPgY0FEXVNbJnqQTRZve0ok0WP0thqoMAQw+5WYH35K21ywkubcbuCLh4GP7xGzQCOvA659q22i2eDLxOaX+9fq6/8rku3/SUSsxyTrZ0NWXzGJ3hf14V5H5CgFavaL7QiUdXVGwxmiyNXkEG3NgPECFRSJDFag4GNBRF2jzJyk5uv3xXl36DF62zdhTq+CmTTX6gTenQ6sfFJ8Pv4h4NKnj414T8wCCs4Sx5ve6f7zUueUdLl+5+o3ct+uUfS2sn6o5xDAlhDe5w6WuBRxyYIo8uz/CYAststQCgujUWaIWBBRELEgoq6J1IQ5hR6jt9VABR2uH1L0HiEuq4qBhsqun6ehEnhtClD0DmCyApe9AJx1X8cL1NW2uTf1U8BGsh3K+iGdxW37smuUNGf0djmAM0SRTA1UMGC7nIJJcxQCLIioayI5YQ7QX/S2LBujIIpL9bYbdnWWqHIPsHAisO87EQt73TvA8KnHf8yJFwPmGNEKcnBT156X/OPY7/kaS0C/87QeTcfUzVnDHKygzBAZbUNWX2pBVK3pMCgEjByooGDLHIUACyLqGjVhLgIDFRR6it52lIgFziYrkHWi1qM5PrVtrgvriPb/CCw8D6jYIRLrbv4MKDyr88fF2r2L+xmuEFrK7FDuKUBCurZjOR4tZohamrxvXBg1YQ7w7kXEGaLIIsvGDlRQKDNELIgoiFgQUddE+gwRoK/obeVFVtZAwBKj7Vg6o27QGmBB9PPHIla7vlysk5r+ZWDFn9I2V/SOCGOg0NBz3LYvNXq7NHzPeWA94G4RaxxS8sL3vMHGlrnI5CgRbWYmiz73svNXgmeGiLHbFEQsiChwbhdQtUccR+oaIkBf0dtGaJdTdCVYYc0C4I1rgdZG0YZ10ydAcq/Anrf/RJF6VrMf2LcqsMeSf5obgN1fi2M9rx8CtAlVUNvlDLghqy8WRJFJmR3qMRiwxmk7lu5QNmet42bcFDwsiChwjlLA1QyYbYA9R+vRhI6eorfVgmiEpsPwS69hIga79gBQc+D493W7gc8eApbOBiADo6YBv3wDiEkK/HmtscCJl4pjts2FRvEKEbdvzwWyBmk9muNL8WwHUHdQJBaGg7ohq4EDFQAWRJEqEgIVALbMUUiwIKLAKS1kqQWAyaztWEJJT9HbBzaKSyPMENkSgMyB4vh464haGoG3pwGrnhafn/swcPGT3YtxH3qluNzyPtDa3PXzUPuUuO3+E/U/AxKfLjYxBsLTNifLkZEwB7AgilSREKgAeFvmmqrD92YHRTwWRBS4aFg/BOgnerv2oHiXG5JodTCCztrm6iuA1yYDWz4QM42XvwSceU/3X2QXjBPrNxqrgF3LuncuakuWfdYP6bxdDhDfSylhDFao3idaeExWb/y8UfkWRFq/GUTB4WoBytaLY6PPEMWlip8zgOuIKGhYEFHglIIokhPmAP1EbyuzQxkDjLPRo/KCsL0ZoopdwMIJor0o1g5c/z4w7KrgPK/JDAy5QhwXvR2cc5JwqEisz7LEAQVnaj0a/4RzHZGyfqjXMGOvzwC8BZGrGWhp0HYsFByHt4g1mjF2IL2f1qPpHknyWUfEtjkKDhZEFLjKKJkhAvQRvW2kQAWFmjT3U9t3mEu+F7HalbvFGo+bvwDyTw/ucytpcz9/DDTXB/fc0Uxplys82zgv+MM5Q1TqKYiM3i4HiDdelHfg2TYXGdS47ZGAKQJe+iVwLyIKrgj4qaCwU2eIoqAg0kP09kEDFkQ9hogXVI2VQPVecd2WD4FXLwEaKkQ4xM1fApknBP+5s08S7Y4tDcC2pcE/f7Ta/rm41Hvcti8l9CWcM0RGD1QAxDvwXEcUWSIlUEGhBCvUsyCi4GBBRIFxtXpf4EbDDJEeorfVGSID7RthiQF6DhHHZeuAVc8Cb94gEsoGXChitZN6hOa5Jck7S8S0ueCoP+INDDBUQeRJmgv1DFFzPXBwkziOhIIIYEEUaSIlUEGRyBki3fphoVhv2mysdlsWRBSY6r2AuxWwxHrX10QyraO3GyrFYm1AbFZqJL1HisvPfg989gAAGRg9HZj6eujXQilpczu/FF9D6p4dXwCQxfdgsoF+7pWWOeVnKFTK1gGyS/xOjJStCFgQRY6mGqB8mziOlBmiBK4h0qVWp9hKY/EvvG+eGwQLIgqMUhikFUZGH3JntI7ePugJVEjNB+JSwv/83aEkzdV4Io/P+zNw0d/CE9WeeYJ48e5uFRHc1D3K+iEjpMv5UkIVavaLDaVDJZLa5RQsiCJH2U8AZLFuU5lZMTq2zOlT6Q8ivCMhy7v9hkFEwStaCqpoSZhTaB29baT9h47W5zRxaY4BrnwFOP2u8O5do7bNMW2uW1qbvRHmRiuIknoBklkUxqHc1Z4FEemZGqgwSttxBBNb5vRp93JxWTBO/3vVHYUFEQUmmhLmAO2jt5X1Q0ZrlwOAjH7ADR8AM1YAQy4P//MPuQKABOxdGZ6NOSPVvlWAs0akOiltkEZhtgDJ2eI4VMEKshxZCXMKFkSRI9ICFQC2zOnVnm/EZeFZ2o6jC1gQUWCiKWFOoWX0thqoMCL8zx0MhWeHJknOH/ZsIM8T6V30jjZjiATKZqz9JxqzTTbU0duVu0VyojnGWMEnnWFBFBlk2TtDFCmBCgBb5vTIWQfs93yvFYzTdixdYMC/bqQpdYbI4Bu7BUKr6G1nHVCxUxxH0gutcFLCFZg213Xq+iEDpcv5soc4WEFpl+s9QqQrRgplzSILImNzlIqiwWQxZut1R5SWuSYH0NKk7VhI2LdKtCen5Il1zwZj0XoAZCCtzd4XFdHSMgdoF719qAiALFr2lF25KTCDJgOfzBaRyId/BrKMtchTc0d2ijcCTFagcLzWo+maUM8Qqe1yo0Nzfq2oM0TVmg6Dukl5x77HYONsqOyP2BTAbANczUB9uffnXAOyLKPZ5UZzq+fD59h51OfH3H7MbS60umRYzBKsZhNsFhNsnkurWRxb1esk2MxmWM2SenuMcj+fS+XxZlOI1/Ts/lpcGrBdDmBBFBJut0gjkyRAMtiisuOq3gvIbsCW6J2ujgYdRG+3utxodcuwmU0wheIXjRH3H9Kb+DSg3wRg+1Kg6G3gnN8H9PCmFhcaml1odbvhdgMuWYbbLcPlluGWxYfLDfVzl1tW7+OW27/e+1jv7b6U3xmS+jkgeT5Tfp1I8F2vKrW5XjmH7+NtFhOSY61IjrMiKdaC5FgrbBY/GgSU2aH804HY5IC+drqhzhC1LYgamlux63A9dpbXYtfherhkGekJNmQkxiAtwYb0RHGcGm87/teqxLM/U+6pIfoHaOSolrnGZhdqm1rQ6pbR6pLR6nYfc+xyy2hxueFSr5fV35OtbjdaXZ77uGW41Ou993Ed/aH8zKg/Z2643DjmZ6rV7Xs/n5859Tzi77IMGbEWM+JsZsRZvZexPse+17d3e7yt7ef+vMiUZRktLu+L5haX98WyctzscqPF5wVyi88L5RaX94V1jMWMDM/3pviwITXe1vHfoEgMVADEL7aELJFiWn8YSMlFU4sLWw/UoLK+WXz9XLL69Wv7dZY9t7f9Wov7yer/S9vHyW3+T44ugIzA5PlbYDWLvwf2OCtSE6xIibMhJd4qPtRjG1I919k911nNnfzN2KMEKrAgIo/HPvsZLywXL54lCTBJEkye4sikfi6pL2pMJqnj+0i+55DanE+WxR8GGd5jtyxDltt+7vZ8Lrf5XPa5DyDDe73bc3281YzctHjkpsWjT1o8xrp+wLkAnMl5kFvdiLWGIT5ZY00tLhxCT+QBcB7egf97fxOKKxuxr6IepVWNaPUUvzbPOzMxVhNiLGbEWMQ7MjFWcSw+zJ7bvffxvX/MUfcftWU1cgCUJw4Eap1IS7CF/h2eIHG7ZTgaW1BR70SLS4bVLMFiMsHic2lVPvdcF6x/myyLP2h1zlbUNbXCnDMJuduXouHHN/Cp/UbUNbtQ29SK2qZW1DlbUNfUijpnK2qaWtVj5bFG+UPXFbHWY4uk5DgrkmMtSIq1IjnOgss2foBeAHaknI6avVWwx3nvF2MxGeINn5rYXkgGUH1wN576aAt2ltdh1+E67K9u9Psc9jgr0j1FUnpCjOfShh6xrbjm0GZIAHbFDkJKnRMp8cb5OT0eV2wqzABqq8rxq+e/w0/7quFya7D1gM7ZzCbEWk2I8xRKbln2KWTksLxgNklAWoIojjKTvIVSemIMrtz2HTIAlCYMhsXRhPREW+cvbA2g1eVGS0wa4lCKV7/4Hv91NGD7oVr1b7KWrGZJnZVRP8wm2Cxm8brAfPT1bT+3miW0uuWjijW5TQHtW9A1u9rez7dg8+WWgaYWN5pa3Khtag3odyAAJMZYOiyieljrcb1nc+oN1qFIOFyHvPR4Q32vsSAKBZ+fR1kW7yq7jr7BAOqbXfj5YC1+PlgrrjB/j3OtwBeHEnHnHz5Fj+QY5KaKYkkpmpTLrKSY0MyahEBtUwv2VjRgX2UDiivqsa/Ce3mgpgkxshM/xwIxrbX4cPVmVCPpmHM0e3451TqDM6Y86SA+sX0MSMDs1VZ8/d2X6h+9zCTPR2IMMpJsyEz0Xpfl+WNoj7MG/cVqi8uNyvpmHKlz4khdM47UOlFR7z0+Uu+5rHOisr454D9MkgRvkWQS7QJmz6XvdRazBLPJBKtJFFNuGW0KmdqmFrS4vM8dh3isjYlBQn0JXnv7XayXu7b+zWySYJYkmEzwXEowq29mSDAfdb16rF6Httcp5zJJkCBBhqxudSV73qTwHnt0cL0syz7HvvcXR+IPYIso+pyt6nVNLU4c7uCbNgkNuCXmR0ACbl6VgX3ffdfmdqtZ8hZRcVZkKAVDYgzSE8QLM6V4EDMtVlhC9MdRlmWUOZqw83Cd+rHrcB12ltchpWEvlsUA1tpSvLxyN7zzaEB6gg19sxLRLysRMRYTKuqaUVHv9Fw2o7K+GS5Pce9obMHuI/VtnnesqQjX2twolTNw7oJtALZ5fk5F4eQ709QjORZDs+0YmmOHPc4akq9Dd5VWNWDFjiNYsaMce3ZsxlIAZmc1figWs0QmCeobGmbfn1GTBLPyZofpqNuOuq9yH+Vn2mL2PsZiEj8fyqVZ8v6MmX1+3ry3i58f358zi6ntY7yPFf9GZ4sbjS0u8dHsQpPnuEE5bvbc1uJGk3rsc73nUqH87q9pavX766x8XdQXyZ6Wp/banGxHtT81tbi8v4PrnKhuaIFbhuc6p/fvNQALWnFjTBEgATd+7sauz74CAKTEW9sUTZme4wzP35Kc1HjkpsUh3qaPl4iyLGNfZQPWl1RjY6kDG0qqUVTmwNMwY4IZ2LJjF7a4+gAAMhJtyE6Jg9V8dOuY1E7LmSg+fL/mvo+xmiW1Ba3NudotdsQbmSHrGOkCWRazp76zY0qxXtvUiqqGZjgaW1BV34zqxhZUN7SgukEcVzW0wOE5djS2QJah/o1tr5C60LQG19uAn925mLxoJ4Cd+PZ345GTGh/+f3gX6eO7PcL8ekJ/3HZWX3W2xXfWxX3UzIz43Od2TzsA0MF93N7rlFYakzrLpMwwidmnNjNKJu99O5uFMkkAJKC2qRUllQ0oqRTFwunbaoAaoMwkYqgP1ThxqMaJtXuPXXRrs5iQmxqnFkhKsZSbGo8+6fFIjAnft54sy6hqaEFxRT32VtRjb0WD50McV9Q3H/fxlpgEHJHSkSFX4N5RZljzhiIvPQF56fFIirWorQ/iwwVnSwfHrW44W1wd37fVDWeLGy0tzfjdwT8jodmJjeYh2BJ3MqQGV5s/elsPHP/fbDOb2rxbqBZRvp972oFqmlpwpE68CFTOf8TnWLm+qqEl4K99cqwFNosJLS5vO43SGnPs/5Py4iLgp+lQgs2MpNgUrHaPwbmtyzEz/Se83/MMJMVakBhjRWKsBUkxFiTGWpAYY0FSrKXNbYkx4iMS3vFXuNwy6ppaUdMk/tDVNLWgtqkVNY2iYKppFJ/nH/oc1lIX9ltykZTWH7lNLahpFAWnWwZaXDIq6ps7/flRSBKQGm8TL8J8CqUMnyIqIykGGZ7b4m3mY4r6FpcbeysaRMFT7lP8lNehoYNvnHpkAAASJCfuHJOO7N7Z6JeViH6ZiUhNsB13zL4znUfqRIFU4fn5qKh34pSSL4AKYJt1EFLMVp8Xp804Utfx16UwIwHDc1MwLMeOYTkpGNw7WZMZ9zpnK1bvqsCKHeVYseNIm4IvGbFALBAvOTHv0gE4fWA2ctOM8+ImlJRZ6MZ2CiazSWpTzFjbKW6C+ftEeaOqvNZ5zO9x25HNiNvTjDopAbUJ+TA3tMLllj0vfFuws5OAtvQEG3LS4pGTGodcT5GUkxqP3NQ4ZKfGIcYSmu/ZwzVN2FDqwMbSaqwvqcam/Q5Ut/P3xxEj2jonFZhw9qknYXhuCnrZYw0xcx0OkiSpBV/88X/VHZfLLaO2SRRJ1Q3N4vunUVwqhdN5u98AHMCO+JHokxiP6oZmpHTnSTUgybJ87CsTA6qpqYHdbofD4UByskF73fXutcnA7q8hT34G1QN+gX2VDSipEsWSUjTtq2xAWXVTp60VaQk29LLHwmKSxHoHSVkX4S3mIHnXSkjwFnXqegmlzdDnccq6LQlievhgTSP2HmlArfP4796lJdiQlx6P/PQE9EmLR35GPPqkJSA/PR5pCTZIiy4G9n4LXLYAGH5197+Wx7P8r8D//gLEJAMzVwIpfdDq+aN32PNHr7zWiXLP5ZG6ZpTXNqnHjsbACxd/+bZmZPi8q5iuHHte0GYkiRe9Ha29UPr+26xDcLnR4rvmwOUtoFo9vd1i/YG7zf1NEnyKGasoYmItSLD5FDLbPwMW/0L0nN+zVexPQ8f33gxgw3+AsbOAiX9Rr5ZlGfXNLk8BJYqk6obmNrOHFWrhIF6gVTY0I9C/NLFWk/q9ZY+zoqy6EcVH6jucebSYJORnJKBfppjxUT4KMxMQ/4+BYuH1rctFGlyw/PtKYOcXwIWPA6fehhaXG1UNzWKGqc1skxPFFQ3YWFqNkspj3121mCSc0DMJw3NTMDzHjuG5KeiflRT0QtzlllG034EVO8rxzY4j+GlvVZuvp9kkYWRuCs7sn4kz+6dh5KJ+kGQ3cO82IKlnUMdCYbD2ZWDJb8T2Bzd8ALdbRnWjeAPsiOdvyNFvhB2qaUJpVWOnf0ckCeiRFCuKpTRRJPkWT73ssX7NCNc0tWBTqQMbSquxwTMDdMBxbGqczWzCoN7JGO55E2F4bgr6bpwP6du/A6fcClz0165+lSgY/jlKpOJO/Q8w8CKtR6MKpDbgqwLyX4VnXVR6P6Qm2JCaYMPw3JRj7tbqcuOAo0ktkEqOuqxqaEGlpx0lnHrZY0Wxk56APp7iJy9dzFglx3bSwpJeKAqiUEdv7/8JWD5PHF/0VyBFtAFYzCZkJcciKzm201M4W12eIsn7R6+81qm+g+hbTDU0u2CzmNSWifQ2lzHHLN4N1voIk0mCzSTBFq7k/77nAHFpYvFt8Tfic+qY2wXs+FwcD7igzU2SJKkzZ73hX2qVyy2L2ZV63xdgomhSP/cpopQ+99KqRpRWtS0g4m1m9PUpepTj4/ar23NFQeQoCV5B5HYDpZ5ABU/CnNVsQlZSLLKSOv45rahzYuN+h/rib0NJNSrqm7G5rAaby2qweI24X5zVjKHZdjGLlJuCETkpyE2LC/jd77LqRrUAWrnzyDHvtOelx+PM/hk4s38mTuub3vZ3YWwK0FgpghVYEBlPqbIhqwhUMJkkpCXYkJZgw4Aex7Z++6ppavF0iDSitKoBpVWNKKn0XFY1oKHZhYM1TThY09Rul4jZJKGXPVadWcpNjUdOWhwyE2Ox83AtNniKoN3l9cc8VpKAAVlJbb73T+iZdOwbbEmecCduzqotx35RDEkmIG+s1qPpMhZE5J+WJm9sbSebslrMJjWM4fR2bq9takFJZSMO1TS1aSFUwiEA5Tqo6yrEbbK6tkIJjpDhaTE85r7i86ykWOSni7F0qyUlHNHbzQ3Au7eKHP9BU4BhXZuJirGYkZ0Sh+yUzl+sOltdsJmNsTi+W8xWYPAU8Y7pprdZEHVm/49is9FYe1DS08wmSW3Z9Ee9s1UUSp4CqqqhGT2SY9EvKxG9kmMD79FPyQXKfhJ7sgRLxU6gqRqwxAE9h/r9sPTEGIw/IQvjTxBR+rIsY391o1ocbSitxqZSB+qbXfi+uBLfF1eqj02Nt4p3xz2zSMNyUo75mtY7W7FmTwW+2S7WAu066gVnUowFY/ule2aBMpCXntDxYONSvQURGY8SuZ0d+IasybFWDO5tx+De9mNuk2XxBkdJlSiWSipFkVRa1YhST9HU7PK+obGqkz3Nc9PivN/XOSkYkm1Hgj9t9QmevYhYEGlLSZfrPdK7f5kBsSAi/1QVA5BFG1dCRrdOlRRrxaDeVgzqbaDWxg6it4Pqiz8CFTuAxJ7AxU/45iqHTKh6wHVp6FWiINr6ETBpPmDtfLYtailx2/0miGIyzBJiLEiIsaBPepDWrHQQvd0tJZ6pnOyTuvU1kiQJOanxyEmNx0VDewEQM2q7y+u8i8hLq7H1QA2qGlqwfHs5lm8vVx/f2x6L4bkpyM9IwLp9Vfhxb1WbUBGTBIzwtMGNG5CB4Tkp/odbHBW9TQbSVAOUbxPHOYEXRMcjSZJY95cYgxHtdIm43TLK65xihqnKO8tUUtmIQ7VNyE9PwPCcFAzLtWNYth3piV3c0FjZn6+eBZGm9nwjLg0at61gQUT+UVrF0grD8kJdd5SNaCt3iemnYH8NdnwJ/PCiOJ7yrNg/h4IrdwyQnCP2rdjxOTDoUq1HpF/bPxOX/c/XdhzB4mk9hWNf8M4Zwg1ZzSYJ/XskoX+PJFx1sijmnK0u/Hyg1rPWQhRJu8rrUOZoQpnjYJvH56TGYdyATIzrn4HTCjNgj+9iwcaCyLjK1gGQAXufsG/sbTJJ6JEcix7JsTg5P4R/y5T9EOvKj38/Ch1ZBnYr+w+N03Ys3cSCiPyjtIqlH79dLmKlFojLJod4cRDMgqWhEvjgdnF8ym1Av3ODd27yMpmAoVcAK/8BbHqLBVFHqkuAQ0WiH7zfBK1HExwhmSFSNmQ9JXjnPI4Yi1mELuSmAKeJ62qbWrBpvwMbSx3YU16PwdnJOLN/JvLT44PTBsuCyLiUdrmcCNuQ1ZfSMud0iLZ+zvqHX8VOoLYMMMcAfcZoPZpuYUFE/lFniKK0ILLFA0m9xQ9+xa7gFUSyDHz0a6DuEJAxADhvTnDOS+0bepUoiLZ/Jorb2GP746PeDs/sUM4pQEK6tmMJlhRPQeQIUkHUWA2UbxXHOeEpiNqTFGvF2L4ZGNu3e23MHWJBZFxHBSpEpFi7eCHucoq2OWUmmMJHWT+Uewpg9S9kR6+Ms4UsaSvaZ4iA0Kwj2vAfYOuHgMkCXP6i4X+h6F6PIUDmQPEHdOsSrUejT9uVdLkIaZcDvDNEDRVA87GpVgFT3n1PLQASM7t/Pr1iQWRMstytQAXDkCRvOyDb5rShtssZe/0QwIKI/KUUAdE6QwSI6G0geNHbVXuBT+4Tx2c/ENz9Uah9kgQMvVIcb3pL27HoUXOD9x2/o+K2DS3WDtg8McPBSJoLc7ucZlgQGZOjVHQdSGag13CtRxNaatLcIW3HEY3cbqB4hTguZEFE0aC5AajZL46jeoYoiNHbbpfY+LK5VsQan/Gb7p+T/DPEUxDtWQ7U8o9oG3u+AVqbxELsrBO1Hk3wSJK3bS4Y64iUhDkWRKRH+z3tcj0Gi3bvSKYEKzBpLvwObRK/G2xJQO+TtB5Nt2lSEH3wwQcoLCyExWLBqaeeiq1bRS92UVERRo8ejdTUVMyePRtyoFubU2hU7RGXsSnRnX4WzJa57/4J7PsOsCUCl70AmKIo/lpraQUiGUx2A5vf03o0+qLEbQ84P/LSJJW2ue4mzbnd3hecGq4fCgsWRMakBipEcLucIpF7EWlGaZfLGwuYjR9JEPaCaNeuXbjpppswb9487N+/H3l5eZg+fTqcTicuueQSjBo1CmvXrsWWLVuwaNGicA+P2lOxU1xG8+wQcGz0dlcd2Ags+4s4vmCeeIFO4TX0KnHJtjkvWfbGbUdSu5wiWDNE5T8DzhrAmgBkDer+uPSMBZExRUOggiJBWUPEgijslPbqCGiXAzQoiLZu3YpHH30Uv/jFL9CjRw/MnDkTa9euxdKlS+FwODB//nz07dsXjz76KBYuXBju4VF7KqI8YU5xdPR2V7Q0Ae/eCrhbgIEXAyOvC974yH+DLxOx0vvXhnazXSM5uEmkKFrjgfwztB5N8KkzRN1cQ6S0y+WMioh3RY9LLYgc2o6D/OdqBQ6sF8eRHKigYMucNlqbgb2rxHEEBCoAGsRuX3zxxW0+37ZtG/r164cNGzZgzJgxiI8X/a7Dhg3Dli1bOjyP0+mE0+lUP6+pqQnNgMkbIhDtM0TBiN7+6k8irjchC7jkH5HXlmQUiVlA4dnArmXApneAs2ZrPSLtKbNDheMjcz+PYEVvl3oCFSK9XQ7wFkROh3ihHekFYCQ4vAVoaQBiksVWDpGOLXPa2P8j0FIPxKdHzEy5pqEKzc3N+Nvf/obbb78dNTU1KCjwtg5JkgSz2YyqqvbfiZ87dy7sdrv6kZubG65hR58KJsypurOOaPfXwOpnxPHkp4GEEO0dQv5R2+be7F4LZKRQ1w9N1HYcoWL37FHS3Za5ku/FZaQHKgBt9+lq4iyRISjrh3qPFJtRRzplhogFUXjt+UZcFoyLmO8zTf8Vv//975GYmIhbb70VFosFMTExbW6PjY1FQ0NDu4994IEH4HA41I+SkiDuQE5tqTNEhdqOQw+6Gr3dWAW8N1Mcn/yryNrjxagGXiw29TuyXbSLRbO6w96ggP4RWhApM0S1ZYCrpWvnaKgEKnaI45zRwRmXnpktQIynKOI6ImNQAz+ioF0O8K4hquc+RGG1J3L2H1JoVhB98cUXeP7557F48WJYrVakpaWhvLztN3RtbS1sNlu7j4+JiUFycnKbDwoBZ603358zRF2P3v74t+KFWFpfYOJfgj8uClxsMnCCJzwg2sMVdnwBQBZ7liT31no0oZGQBZhtIl2wpqxr51Da5dL7R0/iZlyKuGRBZAxqoEKUFERKy5yzBmhp1HYs0aK53jtTXjBO27EEkSYF0e7du3Httdfiueeew6BBovdw9OjRWL16tXqf4uJiOJ1OpKVFyR8dvVJaw+LTvX8Yo1lXWuY2vQ0UvS02ybv8RcCWEJqxUeCUtrmid0SccrTaEcHpcgqTCbDniOOuriOKpnY5BZPmjKOpRqQgAtGRMAeItVIWz5pHts2Fx77VIhjKnut9TRQBwl4QNTY24uKLL8aUKVMwefJk1NXVoa6uDmeeeSYcDgdee+01AMC8efMwYcIEmM3cn0VTTJhrK9DobUcp8PE94vis+0QyFelHv/NES1DNfmDfKq1Ho43WZmDnMnEc6a2cSkHU1XVE0bIhqy8WRMZRtg6ALF6oJvXQejThIUmM3g43tV1uXEQFQ4W9IPrss8+wdetWvPjii0hKSlI/9u/fjwULFmDGjBno0aMH3n77bcybNy/cw6OjMWGurUCit91u4P2Z4r7Zo4Azfxv68VFgrLHAoEvEcbS2ze37DmiuFS8qeo3UejShpQQrdGWGyNUK7P9JHEdDwpyCBZFxKIEK0TI7pFDa5hi9HR67I2/9EKBB7PaUKVMgd/DOen5+Pnbs2IG1a9di7NixyMzMDPPo6BhMmGsrkOjtNc+JJBZrvGiVY2StPg29Clj3b2DL+8CFjwOW9tctRix1M9aJEZMW1CF1c9Z9gT/28BYRMxuTDGQODO649IxriIxDLdijZP2Qgklz4dNYBRzYII4jaP0QoHHKXHuys7MxefJkFkN6wYS5Y/mzjujQFuDLOeL4/P/jDJue5Z8p/qA2VgG7/6f1aMJLloFtS8VxJK8fUti7sReRuiHryZFfOPriDJExyDJQqswQRVlBlMC9iMKm+FsAstjjKrmX1qMJqij6rU5doqwhSu+n7Tj0pLPo7VYn8O6tgMsJ9D8fGHVT+MZGgTOZgcGXi+Noa5ur2AlU7RHpa4Vnaz2a0FNniLpQEEXThqy+WBAZQ81+oO6gCO/pNVzr0YSXMkPElrnQi9B2OYAFER1PkwNoOCKOIyhJpNs6i97+3/8BhzaJZL5L/xlRiw4jlpI29/PHIlI0WiibseafAcQkaTuWcFBniEoD34xXTZiLgv2HfLEgMgZldqjHINHaHU0SGaoQNsqGrIUsiEgvHPvFHkGhpLzgT+wRHS+W/HW8lrnilcDKp8Txpf+MnqQfo8s+SQRmtDR4W8iiwfYoiNv2lZwNQBKzt4Fs5FhXLmbSIEVfOxILIv801wNfPuItnMNtf5S2ywFsmQuXmgPAkW0AJCDvdK1HE3QsiIzE1Qpsfh94+ULgiUHA304Alv4OqNwTmuerZKBCuzqK3m5yAO/NACADI68HBk7SZHjUBZLknSWKlra5xmpg73fiuP9ETYcSNhYbkOTpew+kba7U8yI3c2D07cfGgsg/K/8BfPsE8PpVXd/4tzuiNVABYMtcuCizQ72GR+TG1CyIjKChUvyi/cdw4K0bRUwuIBKP1jwP/PMk4L/XA/vWBN4GcjwVDFRoV0fR20t/Bzj2Aan5wAVzNRkadcPQK8Xlzi/Fz1yk2/UVILuAjBOAtAKtRxM+yjoiRwBJc9HaLgewIPKHs1b8LQaApmrxxlg4N3p2tXr2IEJ0zhCxZS48IrhdDmBBpG+HtgAf3gXMHySm4mtKgfgMYNxs4J6twPXvAf0mALIb2Poh8PJE4KUJQNG74hdkd1VyU9Z2KdHbgLdo3Pw+sOE/gGQCLlvAFkMjyjwB6DkMcLeKCO5It/1zcRnpm7Eezd6FYAUlUCH31OCPR++UgqipOrwv8o1k7cviDTJ7H7HNwp7lwOpnwvf85VtFu68tCcjoH77n1QulZa65Dmhu0HYskUqW227IGoG4MYreuF1iofOa573VOAD0HAqcOhMYcoXYTBIAknsDfc8BDm8FVj0DbHxT9BG/fZP4o3/qDOCk64FYe9fGUsFNWTuUVij2IqrcDdhzgCV3i+vPuAfoE4UvmiLF0KuAgxuBTW8DJ/9K69GEjtsF7FAKoihZP6RICTB629USnRuyKmJTxKXsBpw10dcy2JmWJvH3FwDOvh9wNYu/B1/9SSQ39hwa+jGocdsjRWpmtIlJAixxQGujaJuz5Ws9oshTuVv8zjRZgT6naT2akOAMkV40VgPfPQ08NRJ44xpRDEkmYNBk4KalwG0rgJHXeoshX1knApOfBn5TBJx1v5hFcpQAnz8EzB8MfPpg1zYi5AxRx5Q2woqdwAd3iHaSXsOBs36n7bioe4ZcAUAC9q4USWSRqnQt0Fgp3iyJtlkPe4649HeG6OAm8UIrNiU6tx+wxopZD4Btc+1Z/zpQdwhIzhFvqIyaBpwwSRRG70wHWhpDP4ZoDlQAxBrQRAYrhJTyBn3uKYAtQduxhAgLIq2Vbwc+vle0xX3+EFC9V7QonH438OuNwC9eA/LG+hfdnJgFjH9AFEaXPCXWBjTXiqn7fwwH3prmfSepMw2V3j9+jNw+llIk/vCiWIthiQUuf1Es2ibjsmd703OK3tF2LKGkxG33Ow8wR1mjgL2PuPR3hkhtlzslujZk9cV1RO1ztQIrnxTHp98lfv9LEnDpU2Khf/nPwBd/DP04Sn8Ul9EYqKBQghVYEIVGhLfLASyItOF2i/79f10OPDMa+OElEZCQNQi45B/Ab7YA583xtnYEyhoHjLoRuH01cO07QOF40e6w+T3gpXOBhROBLR+ItpmOKAlzSb2jb08DfyhFovIC4bw/izUoZHxKuEIkp81FW9y2r0A3Zy1ZIy6jsV1OwYKofUXviO6L+AyRLKpIyAAmPyuOv18A7PgidGNw1orCCwCyR4XuefQuwROswKS54HO7gT0rxHEEbsiqYEEUTs5aYM0LwNMnA4uvEjMLkMT0+o0fATO/E9PtwSpATCag/wTghvfFuUdcJ3akL1kDvHmDaM9b/Vz7+xlx/dDx+X5d+p4DjJ6u3VgouAZNFn3SBzcBh3/WejTBV70POLxZtOT2O1fr0YSfEqrgdIiF8J0pUWaIojBhTsGC6FhuN/DtfHF82u3H/t3uP0Gs4wWA928Xe1mFQtk6ALJo2UvqGZrnMAK2zIXO4S1AwxHAmhDRRTcLonCo2AUsvR/4+4nA0vvE2pwYO3DancBd64BfLhbTkP60xXVVj8HAlGeAu4uAcfcBcWmiPe/T+8U6o89/3/YdU3X9ENvl2pXWV0zRJ2SJdwKjtZUmEsWnifRGACh6W9uxhIIyO5Q7JiL3kuhUTKL3BX5ns0Q1B0Q8t2SK6BcCnVKCFFgQeW37RMzMxCR3/IbYhEeAzBPFrMWHs4K7LYZCaYPPieLvT4Atc6GktMvlnRbRywL4Ki5UZBnYtQxYfDXwz1HAmufEep6MAcCkvwP3bAHO/7/w7/+R1AM45yHgN5uBi58A0vuLd0q/+6dYZ/T2zSJRiTNEx2eNBe5YA9z5PZDcS+vRULD5ts2F4kWMltR2uSiL2/alzBJ1FpyhbMiaNTi6o/TVGaJqTYehG7IMrPi7OD7llo6TXK1xwBUvic6M7UtFPHew7fesH4rWQAWFEr1dd0jbcUQiJVAhgtvlAMZuh8aGN4AV84Ej27zX9T8fOPU2sZ5HD7MJtngRK3zSNGDnF8Cqp8U3fdHb4sPk+dZgwlzHlBcJFHlOuFC0B1QVixcckbJYubne+8ctmguilD4iXr2zYIVo3pDVF1vm2tqzHCj7SUQ9nzrz+PftOUTMFH32IPDZQ0D+mUDmgOCMQ5Z9Zogi5HdUVymbs9aHqDUxWrlageKV4jhCN2RV6OCVeQQ6VCSKIVuS6CGe9RNw7ZuiX18PxZAvk0m8MLrxIxHtPfyXYv2E27Oxa0aQfnETGYktARg4SRxHUrjC7uWAyykKgsyBWo9GO+rmrJ1sRxDNG7L6YkHUljI7NOpG79qV4zl1ptiTqLUReHc60NocnHHUlAF1BwHJLLZ9iGZsmQuNsp9Ed1NcKtAjDHtqaUhnr84jxOhbgAseE21xFz5mnLazXsOAy54H7t4EnP0AMPEvwXsni8hohl4lLoveFe+SRYIdPulyoVyzqHf+bM7a6vQsWAeQwxkiACyIABGysecb0UVx2p3+PcZkAqY8L76OBzYAXz8anLEo+w9lDYrYvWH8lsBQhZBQ1g/ln6m/N/SDLLL/dVpJzQPGzABik7UeSdck9xI7bo+dpfVIiLTTd7wIH6k/LHr/3W6tR9Q9ssz1Qwq7H9HbBzaKzTXj0xkuw4LIS0mWGzY1sK0xknsBl/7Tc44nvTHG3cFABS9lhqilXrQGU3Dsjvz9hxQsiIiI2mO2ihZSAFg6G3hurJgtMmphdHAjUHtArI3KO0Pr0WjLniMujzdDpAQq5J4a3bNpgCgKAdEOrrxAikaHNot0OUjAGXcH/vgTL/HsVyQD783ofoHJQAWvmETA6ok+5yxRcLQ0etdRFp6t6VDCgQUREVFHzv2jaB+NsQPlW4G3bzJuYaTMDvUdL1ISo1lKH3FZdwhoaWr/PuqGrFHeLgeIojBnNNBcB/zrMrGfXqSlL/rj2yfE5aDJQEb/rp3jgnlixrGmFFhyT9e/jq5Wn5ZOFkQA2DYXbCVrxJrTpN5Aej+tRxNyLIiIiDpijRXto3dvbKcwOk3sVO92aT1K/2z/VFxGe7scIGY8LHHiuGZ/+/dRN2Q9JTxj0jOzVQTvDLsakF1iP70P7hTrrKJF5W7x8w4AZ97T9fPEJAKXvyiCEDa/C2z8b9fOU/4z0NIgwpsYfiQobXP1LIiCwrddLgpmyVkQERF1Ji7FpzB6UOw7Uv4z8PavxIzRprf1XRjVHfa21/SfqO1Y9ECSvOs/2kuac5QCtWXiRWvvk8I7Nr2yxgGXvQBM/D+xUe36fwOLJgG1B7UeWXis/Acgu4F+53U/0S3nZPEGCwB8/FsR7x8oJVCh9wjAZO7eeCKFEr3NGaLgUAIVIjxuW8GCiIjIX3EpwNm/E0mM4x/yFkbv3Aw8e5p+C6Mdn4vL3iOBpJ7ajkUv7MdJmlPa5XoOFXu2kSBJwNg7geveAWJTRCz5grO9i/sjVc0BYP1icXzmvcE555n3ALljRKTxu7cFnmTJ/YeOxZa54GlyeFsyoyBQAWBBREQUuFg7cNZ9bQujI9s8hdEY/RVGarvcBdqOQ0/U6O3SY29ju9zx9T0HuGWZ2Muq9gDwyoXegiESrXpaJA72GQvknRacc5rMwOUviJa3ktXe9Un+YqDCsdgyFzzFK8WMaFpfbwhNhGNBRETUVW0Ko9+Ld82PbPcWRhvf0r4wanUCu/4njtku53W86G3fhDlqX3pfYPqXwAmTRLHw/kzg0wciZ88uRUMlsPYVcdydtUPtSc0HJnk2ef16rv8zbc5a4PBWccwZIq9EzhAFTZS1ywEsiIiIui/WDpw1WxRG5/gURu9OB545Fdj4pnaF0d6VIh0ssQfQa4Q2Y9AjJWnu6Ja5lkaxeSbAhLnOxCQBV/8bOOt34vPVzwL/vlwUEZFizQtib5ueQ4F+E4J//mG/AIZcIcIq3r0FcNZ1/piydQBkIDmHLbC+lBkiFkTdt+cbcVnAgoiIiAIVmwyMO6owqtghXuhoVRht96wf6j8x4ncaD4i9g1CFsvWAu1W8uFKKJuqYyQSMfxD4xb/EHld7lgMvjgcObdF6ZN3nrAXWPC+Oz7w3NElbkgRMmi+Km8rdwKf3d/4YtV2OgR9tJHhCFRwlgKtF27EYWd1h4LDn5zf/TG3HEkb860hEFGxtCqM/AHGpPoXRKcCG/wantai1WayB2f8jsG0p8OMiYPnjwMf3Av+9DnjpPOCnV8V9uX6oLWUNUc3+tkWq2i53SlREzQbNoEuB6V8AKXkiNe2lCcCWD7UeVfesfQVoqhZ7sJx4aeieJy5FrCeCBKz7V+dfNwYqtC/zBLEmq/YA8PnvtR6NcSmzQz2HAgnp2o4ljCxaD4CIKGLFJgPjfguccivw/QKxOLtiJ/DercDyx8T6oyFXAmafX8WyLHawrzvk+TgsLmsPeo+Vy0Y/W5MSMqNip/GAJPUSsdruVvG1tWeL65Wd2XMYqBCwHoOBW78G3pomZorevB44637RUme02cmWJvHzCgBn/Cb00db5ZwBn3C3CFT66SxQ7yb3bvy8DFdqnFJZvXCNm9noOA0Zeq/WojEdZPxRF7XIACyIiotBTCqNTbxOF0Xf/BCp3Ae/dJmZ0Mga0LXTcAbR7mCyivSsxy+eyp8/nPYCsgWJDSPIymYHkbMCxT7TY2LNFMVriM0NEgYtPA657F/jiD2JN0fJ5wKEi4LLnxZojo1j/uvhZTM4Bhv4iPM959oPArmViDdv7M4Hr3ju2kHTsFzMgklnsQURtDZwk9nj6ei6w5DciCTFnlNajMpbdLIiIiCiUYpLEWoRTbgW+f9FbGFXuOva+cakdFzjKcVJPsU7JaO++60VKriiIqkuAPmOA6r0istdkZQBFd5gtwAVzgR5DgCV3Az8vEe2bv1wMpBVqPbrOuVrFRqwAcPpdgMUWnue12IDLXwJeGAfs/hpY8xxw2h1t76NsyJo1CLAlhGdcRjPuPuDgJvF9999rgVuXA0k9tB6VMVQVi9+DJguQN1br0YQVCyIionCLSRIRvqfcAmx+37uIP8lT8CRkApYYrUcZ+dTNWT3BCsrsUK/hgDVWmzFFkpHXinUdb1wLlG8FFowHrnpF7GOkZ5vfFS8K4zOAkdeH97kzBwDn/x/w8T3Al4+ITTF7DvXezkCFzplMYkbypQli4+w3rwdu/Ii/U/2hrB/KPjnqugr4tiIRkVZikoCTrgdOvgkYeBGQPUpsgsc/3OGhBCsoexGxXS74ck4W64qyTxYBBf++Alj1jGhP1CO3G1gxXxyPmQnY4sM/hpN/BQy4UOzv9M4tIgpeUeopiBiocHwxScDUxUCMHShZAyy9T+sRGYPaLjdO23FogAURERFFJ3WGyFMQlbIgConkXsC0j4ER1wGyG/jsQeC9GW1f6OvF9qViNismGRg9XZsxSBIw+WkRI12+VcwUASINsWydOGagQufS+wJXvgxAEgmca1/WekT6JsveGaIo2pBVwYKIiIiikzJD5CgFmuuBg0XicybMBZ81VrzIv/BxEQiw8Q3glYuAmjKtR+Yly8CKv4vj0dNFaplWEjKAKc+K4zXPAzu+BA5vFZvE2hJFKyJ1rv8EYMLD4viT2cDeVdqOR8/KfxZrKC1xUbkpNQsiIiKKTnbPxqvVJcD+nwDZJZLnlAhuCi5JEkmL178nQkPKfgIWnA3sW6P1yIQ9y8UaHUssMOZ2rUcD9D8POOU2cfz+TGDHZ+K498jQx4BHktPvBgZfLtZqvnm9SOqjYyntcn3GRGXbNgsiIiKKTvYccdlSD2z/VByzXS70Cs8S64qyBoto60WTgJ9e03pU3tmhk24EEjO1HYvivDlA5oninftlfxHXcf1QYJQWxB5DgfpykTynx3ZNrUVxuxzAgoiIiKKVNVas0wCAze+JS7bLhUdqPnDz58CJl4p9tz6cBXx8r9gQVQula8ULQpMFGDtLmzG0xxoHXPEiYLaJ9VeACF+hwNgSgKmvA3FpYh3Wkt/oN9hDC65WoPhbcRyFgQoACyIiIopmyjqiGk8bDWeIwicmEfjFa8D434vPf3gJePEc71qucFKS5YZN9X5P6EXPocC5f/R+zkCFrknNA65aJNawbfgPsPo5rUekHwc2AE4HEGuP2j3YWBAREVH0UtrmAMAcA/Qcpt1YopEkAWfNBq55S+y/dXgz8OJ4YOVTIgI7HA5tAbZ9DEACzrg7PM8ZqDF3iLUw438vUvuoawrPEvs8AcDnvxcb4JJYPwcA+WdG7fo0FkRERBS97D6zAb1HAhabdmOJZgMmAjNXefff+eIPwGuXeveICqVvnxCXgy4FMvqH/vm6wmQS64nOmq31SIzv1BnA8GtEiMpb04CqYq1HpL090bv/kIIFERERRa+UPt7j3OiLmtWVxEzgl/8BLvkHYI0HilcAz50ObHwrdM9ZuQcoelscn3FP6J6H9EOSgIufAHqfBDRWAW9cK2L3o1VLE7BvtTguiM5ABYAFERERRTPfGaLcU7UbBwmSBIyaBsz4VqyVcTqAd6cDb/9KvHgNtpX/EGEF/SYAvUcE//ykT9ZY4Op/i1CVQ0XA+7dHb8hC6Q9AaxOQ2COq97diQURERNHLdwE9E+b0I70v8KvPgLMfEIvgi94Rs0XKXinBUHMAWP+6OD7z3uCdl4zBng1c/S/AZAW2vO9tnYw2vu1ykqTtWDTEgoiIiKJX5kCxkHjoVUBSD61HQ77MFuDs+0U8d1pfkQT42qXApw8GJ5571dNivVKf04C8sd0/HxlPnzHARY+L46/+BGz/XNvxaEF5kyGK2+UAFkRERBTNzFZg2hLgipe0Hgl1JOdkYMYKYNRN4vPVz4gkuu7EczdUAmtfEcecHYpuJ//K870lA+9MB47s1HpE4eOsBfb/KI6jdENWBQsiIiIi0jdbAnDJk8Av/+uJ597SvXju7xcALfVij59+E4I+XDKYCx8HcseINWtvXAM01Wg9ovDY+51I20vNbxswE4VYEBEREZExnHBB9+O5nbXeTTnPuCeq102Qh8UmNglO6g0c2Qa8d1v49sHS0p5vxGWUt8sBLIiIiIjISNrEcyf4xHO/6V9S2I+LgKZqsS5p0ORQj5aMIqkHMPXfYoPmbZ8Ay+dpPaLQU9YPRXm7HMCCiIiIiIxGjede4RPPfUvn8dwtTcB3T4vjM34DmMxhGS4ZRPYoUWgDwPLHgK0faTueUKo/AhzaJI7zo3dDVgULIiIiIjImNZ77QRHPvfldTzz31+3ff8NioO4gkJwNDLs6rEMlgxjxS+DUmeL4vRnA4a3ajidUileIy6zBYtY1yrEgIiIiIuMyW4Czfwfc/IVPPPfkY+O5Xa3At0+K47F3iXUjRO2Z+GcRx99cB/znl6HZFFhru332HyIWRERERBQBckZ1EM/taQva/C5QvReITwdOukG7cZL+ma3AVa8C9j5A1R7Riul2aT2q4NrD9UO+NCuIKioqUFBQgOLiYvW6oqIijB49GqmpqZg9ezZkfxZHEhEREQEdxHOfA6z8B/DtE+I+Y24HbPGaDpMMICEdmPo6YIkDdi0Dvpqj9YiCp7oEqNwt2kzzTtd6NLqgSUF05MgRXHzxxW2KIafTiUsuuQSjRo3C2rVrsWXLFixatEiL4REREZGRKfHcJ1zkief+oyiObEnA6Olaj46MotcwYMoz4njlP4BNb2s7nmBR4rZ7jwRik7Udi05oUhBNnToVU6dObXPd0qVL4XA4MH/+fPTt2xePPvooFi5cqMXwiIiIyOgSM4Gpi4FLnhLx3ABwynQgLkXTYZHBDLkCOP1ucfzBncCBDZoOJyjYLncMixZPumDBAhQWFuLuu+9Wr9uwYQPGjBmD+HgxjT1s2DBs2bKlw3M4nU44nU7185qaKNlVmIiIiPwjScCoG8ULv72rgKFXaj0iMqJz/wgcKgJ2fgm8cS1w2zdAfJrWo+oaWeaGrO3QZIaosLDwmOtqampQUFCgfi5JEsxmM6qq2k/2mDt3Lux2u/qRm5sbsvESERGRgaXmizhls1XrkZARmczAFS8BaYWAowT4ZLbWI+q6IzuA2gNiA9rcU7QejW7oJmXOYrEgJiamzXWxsbH4//buPqqqOt/j+Ofw6BOHo0k4BYTAqElajpGlc726qtFcGOpM+cBcBS8mmuZoyys9zaR2y0nXWPeaJtQMOpo610bUabRrOrksb17BwGuYDohCaKaGgmIIsu8fZzxFaqKcw8a936+1znLv87B/33N+67s8n7U3v1NdXX3F5z/zzDM6c+aM51ZWVtYcZQIAAMBuWreXRmRJDj9p31rps3VmV3RjLl0uF9VHCmxtbi0tSIsJRB06dNCJEyca3FdVVaWgoCv/TkBwcLCcTmeDGwAAAOATEfdKP53h3v7LDKnquLn13IhLgYjL5RpoMYEoISFBn3zyiWf/8OHDqqmpUYcON+k1mgAAALCWf54ldeohnf9a2viU+29ybhb1F6WSHe5tAlEDLSYQ9e/fX2fOnNHy5cslSfPmzdNDDz0kf39/kysDAAAAJAUEScOXSv5B0sHN0qcrzK6o8Q7vkL45LQU73Utuw6PFBKKAgABlZmYqPT1d4eHhWrt2rebNm2d2WQAAAMC3wuOlgc+6tzc/I1UcMbeexqg6Lq1Ld293T5L8TVlousVyGEbLOtdXXl6u3Nxc9e3bV2FhYY1+XWVlpUJDQ3XmzBn+nggAAAC+U39R+sMjUtkuKfqfpLEbJL8Wc56hoYu10rJHpdKdUlg3Ke0DKTjE7Kp87nqyQYubudtvv11JSUnXFYYAAACAZuPnLw1bIgW2cV+K9r9Lza7o6v77eXcYCnZKI1faIgxdrxYXiAAAAIAW75ZY6eE57u0PXpROHDS1nCsqWCPtetO9PXyp1DHO3HpaKAIRAAAAcCMS0qSYgVLdN1JOunSxzuyKvnVsr7Rxmnu7/79J3YaYW08LRiACAAAAboTDISW9IQWHSuV50kcLza7Irfprac0vpbrzUtzD0oAMsytq0QhEAAAAwI0KvV0a8qp7e/s86ViBufXUX5TeTZNOH5HaR0s/z3L/zROuikAEAAAANEXPkVK3RKm+zr28dV2NebX87WWpeKsU0Nq9iELr9ubVcpMgEAEAAABN4XBIQ1+X2nSUviqU/vbv5tSx/y/SjgXu7aRFUqe7zKnjJkMgAgAAAJqqbUd3KJKkj/9DKv2kecc/cfDbH1+9/0mpxy+ad/ybGIEIAAAA8IY7E6W7R0sy3OGk5mzzjFtTJa1Jli5USXf8VHp4dvOMaxEEIgAAAMBbBs+TnLdLFSXSll/7fjzDkHImSScPSiG3SY/9QfIP9P24FkIgAgAAALyltcu9FLck5b4tFW317XgfLZT2b5T8g6SRf5Ta3erb8SyIQAQAAAB4U+xAKWGCe3v9FOl8hW/GKdoqbZvr3h4yX4q41zfjWByBCAAAAPC2h2dLHWKkqqPSplneP37FEendf5WMeuknY6XeKd4fwyYIRAAAAIC3BbWVhi+VHH7S3jVS4QbvHbv2vLTml+4zT7f9RHpkvveObUMEIgAAAMAXIu+T+k1zb//lV9LZr5p+TMOQNv5K+nKv+3ePRv5RCmzV9OPaGIEIAAAA8JUBz0jhd0nVp9xBxjCadrzdb0l7V0sOf/eKcqERXinTzghEAAAAgK8EBEvD35T8AqUD70kFq278WEf+R9qc4d5+eI7Uub93arQ5AhEAAADgS516SAP+EWQ2zZJOl13/MSqPSf81Tqqvk+76ufTAk96t0cYIRAAAAICv9fuVFJEg1VRK65+U6usb/9q6C+4wdPa4dGt36dH/lBwOn5VqNwQiAAAAwNf8A6Rhb0oBraWS7e6/BWqs95+VynZJwaHSyBXuFezgNQQiAAAAoDl0jHP/PpEkbfm1dLLo2q/Jf0fanSXJIf08S7ol1qcl2hGBCAAAAGguCRPciyHUnZdy0qWLdVd/7tF898p0knu1ui6DmqNC2yEQAQAAAM3Fz09KWiwFO6Uvdks7X7/y886dktb8i3SxRuoyWOo/s3nrtBECEQAAANCcXJHS4Hnu7b+9In35fw0fv1gnvTteOlMqdYiVhi91Byn4BJ8sAAAA0NzuGSN1HSLV10rr0qW6mm8f2zZXOvShFNjWvYhCa5dZVdoCgQgAAABobg6HNPR1qc0t0vF90of/OGNUuF76+DX3dtIiKby7aSXaBYEIAAAAMEO7W6XEhe7tj1+T9vxRypns3u87VbprhGml2QmBCAAAADBL9ySpx+OSUS9tmCJdOOtehe7BF82uzDYIRAAAAICZhrwqhdzm3nZGSL/4g/uHXNEsCEQAAACAmVq3lx5f7l5kYcxqqW1HsyuyFaInAAAAYLbIBGn0KrOrsCXOEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsiEAEAAACwLQIRAAAAANsKMLsAbzEMQ5JUWVlpciUAAAAAzHQpE1zKCD/EMoGoqqpKkhQZGWlyJQAAAABagqqqKoWGhv7gcxxGY2LTTaC+vl5Hjx5VSEiIHA6H2eWosrJSkZGRKisrk9PpNLsc+ABzbH3MsT0wz9bHHNsD82x91zPHhmGoqqpKt912m/z8fvivhCxzhsjPz08RERFml3EZp9NJU1occ2x9zLE9MM/WxxzbA/NsfY2d42udGbqERRUAAAAA2BaBCAAAAIBtEYh8JDg4WL/5zW8UHBxsdinwEebY+phje2CerY85tgfm2fp8NceWWVQBAAAAAK4XZ4gAAAAA2BaBCAAAAIBtEYgAAAAA2BaBCLgBU6dOlcPh8Nzi4uLMLglAI506dUqdO3fW4cOHPffR08DNZf369YqJiVFAQID69Omj/fv3S6KXrejUqVPauXOnTp486bMxCERetm/fPiUkJKh9+/aaOXOmWLPCmvLy8vTee++poqJCFRUV+vTTT80uCV5ypS/L9LV1nDx5UomJiQ3mV6KnreZqX5bpZWsoLi5Wamqq5s2bp/Lyct1xxx1KS0uTRC9bzerVqxUXF6cnn3xSUVFRWr16tSTv9zKByItqamo0dOhQ9e7dW7m5uSosLFR2drbZZcHL6urqtG/fPvXv318ul0sul0shISFmlwUvuNKXZfraWkaNGqVRo0Y1uI+etparfVmml61j//79evnll/X4448rPDxckyZNUm5uLr1sMadPn9bUqVO1Y8cOffrpp1q6dKlmzZrlm1424DXr1q0z2rdvb5w7d84wDMPIz883+vXrZ3JV8La8vDyjXbt2RmxsrNGqVStj0KBBxpEjR8wuC17w4IMPGq+99pohySgpKTEMg762muLiYsMwjAZzTE9by8aNG40lS5Z49rdt22YEBQXRyxa2ZMkSo3v37vSyxZSWlhorVqzw7BcUFBghISE+6WXOEHlRQUGB7r//frVp00aS1LNnTxUWFppcFbxt//79io+P16pVq1RYWKjAwEBNnDjR7LLgBZmZmZo2bVqD++hra4mJibnsPnraWhITE5Wenu7ZP3DggOLi4uhli7pw4YIWLFigyZMn08sWExkZqeTkZElSbW2tFixYoBEjRviklwOaXC08Kisr1blzZ8++w+GQv7+/Kioq1L59exMrgzclJyd7GlSSFi1apJiYGFVWVsrpdJpYGZrqSl+W6Wvro6et69KX5enTp+vQoUP0sgU9//zzateunZ544gkFBgbSyxZUUFCggQMHKigoSJ9//rnmzp3r9V7mDJEXBQQEKDg4uMF9rVq1UnV1tUkVoTm4XC7V19fr2LFjZpcCH6Cv7Yeeto7vflmml61ny5YtevPNN/XOO+8oMDDwssfpZWvo2bOntm7dqvj4eKWmpvqklwlEXtShQwedOHGiwX1VVVUKCgoyqSL4wowZM/SnP/3Js7979275+fkpMjLSxKrgK/S19dHT1vT9L8v0srUcOnRIycnJWrJkibp37y6JXrYqh8OhXr16KTs7W+vXr/dJL3PJnBclJCTorbfe8uwfPnxYNTU16tChg4lVwdvuuecePffcc+rUqZPq6uo0depUpaSkeK5lhbXQ19ZHT1vPlb4s08vWcf78eSUmJmrYsGFKSkrS2bNnJUl33303vWwh27Zt06ZNmzR//nxJ7is2JKlbt27e7+UmLcmABmpra42wsDBj2bJlhmEYxsSJE43ExESTq4IvZGRkGC6Xy4iMjDSeeuop4+zZs2aXBC/Sd1Ygo6+t6btzbBj0tJVUV1cbd955pzFhwgSjqqrKc7tw4QK9bBHr1q0zJF12KykpoZctpLy83AgJCTGWLl1qlJaWGmPHjjUGDRrkk/+XHYbBr5J5U05OjsaMGaOQkBBdvHhR27dvV3x8vNllAbgODodDJSUlio6OlkRfAzeTnJwcDR8+/LL7S0pKlJ+fTy8DN5H3339f06dP1xdffKFBgwZp8eLFCgsL8/r/ywQiHygvL1dubq769u2rsLAws8sB4AX0NWAN9DJgDd7sZQIRAAAAANtilTkAAAAAtkUgAgAAAGBbBCIAAAAAtkUgAgAAAGBbBCIAAAAAtkUgAgAbevHFF9W2bVudPn1akvuXvh0Ohw4fPtykY6akpHilPm/Zu3evevXqpVatWunHP/6xamtrf/D5KSkpcjgccjgcCg0N1ZAhQ1RcXNxM1UrZ2dkaMGBAs40HACAQAYBtVVdX6/e//73ZZfjU7Nmz9cADD+jIkSNasWKF/P39r/ma9PR0VVRUaNeuXQoMDFRqamozVAoAMAuBCABsyt/fX4sXL5aVf47u1KlTuu+++xQeHq4+ffrIz+/a/+0FBwfL5XKpW7dumjlzpvbs2dMMlQIAzEIgAgCbGjBggE6cOKFNmzY1uP/7l21dupxOkqKjo5Wenq5OnTpp1qxZSkpKUlhYmPLy8iRJX3/9tQYMGCCn06nk5GSdO3fOc5zNmzerR48ecrlcSktLU01Njeex6OhoffDBB3r22WfVqVMnFRQUNOo9rF27Vl27dlXHjh01ZcoUffPNN5LcZ3kcDoe2b9+u1NRUORwOpaenX9fnU1tbqz//+c+KiYm55njfv1zwww8/VHR0dIPH5syZI5fLpTvuuEM7duzwPHfu3LkKCwtTXFzcZeFrwYIF+tGPfiSn06nRo0c3+MwAAN5BIAIAm2rXrp3Gjx+vRYsWXdfrzpw5oxdeeEGvvvqqUlJS1L17d23evFmStHHjRo0bN065ubk6cOCAZs+eLUkqLi5WUlKSpk+frry8POXl5Wn+/PkNjvvCCy/o6NGjWrVqlWJjY69ZR25ursaNG6ff/va3+uijj5Sbm6uMjAxJ0sKFC1VRUaF+/frpjTfeUEVFhRYuXNio97dkyRK5XC61bdtW7733nt5+++1rjnctf/3rX1VUVKQ9e/aoX79+eu655yRJGzZs0MKFC/Xuu+9q+fLlWrlypec1n3/+uTIyMrRmzRrt2bNHRUVFys7ObtR4AIDGIxABgI1NmTJFW7ZsUVFRUaNfM27cOMXHxys8PFzDhw9X586dPYsV9OnTR6mpqerSpYsyMjK0du1aSdKqVavUq1cvjR8/XrGxsUpPT9eGDRsaHDc0NFTZ2dkaOHCg2rVrd806srKylJycrGHDhqlbt2763e9+p8zMTBmGodatW8vlcikgIEBt2rSRy+VS69atG/X+kpOTlZ+fry5dumj8+PFKSEi45njX4u/vr8zMTMXExCglJUVlZWWSpHXr1ik5OVn9+/dX3759lZaW5nlNq1atJEk1NTWKiorSrl27NGHChEa9BwBA4xGIAMDGYmNjNXjwYC1evPiqz6murm6wf+mL+qV/v6tz586e7aioKB07dkySVF5erj179sjlcsnlcunpp59WaWlpg9dOnTr1umovKytrcDlbTEyMzp8/r5MnT17Xcb7P6XR6Lg3MysryBJ7rGe/7n9kDDzzg+byCgoI8xzx27JgiIyMbHPOS6OhovfXWW8rIyNAtt9yikSNHNvm9AQAuRyACAJubNm1ag7M1DodDFy9e9Ozn5uY2+liXznxI7hAUHh4uSYqIiNCjjz6q/Px85efnq6CgQFu2bGnw2rZt215X3VFRUTp06JBnv7i4WG3atFHHjh2v6zhXM3bsWH355ZfaunXrNce71mfmdDqvOMatt96qo0ePeva/GxK/+OIL9ezZU3l5eSotLVVFRYXmzp3rlfcGAPgWgQgAbO6hhx5S165dPfsRERH67LPPVFFRoePHj2vBggWNPtbOnTu1YsUKFRUVaf78+Ro2bJgkafTo0dqxY4f+/ve/S5Jef/31Ji9nnZaWppUrVyonJ0cHDhzQ008/rSeeeMKzAERTOZ1OjRkzRpmZmdccLyIiQrt371ZNTY0OHjyorKysRo2RlJSklStXaufOndq1a1eD1xUWFuqRRx7Rxx9/rHPnzsnhcKi+vt4r7w0A8C0CEQBATz31lGd74MCBGjx4sHr06KGhQ4fqpZdeavRxBg0apKysLPXu3VtRUVGaM2eOJPelYMuWLdOMGTMUHx+vffv2adWqVU2q+d5779WyZcs0a9Ys9evXT71799Yrr7zSpGN+36RJk5STk6OvvvrqB8cbNWqUoqKi1KVLF02YMKHRdYwYMUKTJ09WUlKSxo0bp6SkJM9jP/vZzzRx4kQ99thj6tKliwzD8CzGAADwHodh5R+gAAAAAIAfwBkiAAAAALZFIAIAAABgWwQiAAAAALZFIAIAAABgWwQiAAAAALZFIAIAAABgWwQiAAAAALZFIAIAAABgWwQiAAAAALZFIAIAAABgW/8PF9ruYhv1NAkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LhUKIRCKor6/XfZ0ZM2agqqpK/hk8eHC2PgJBEARBEARBEAEh6wHRpEmTsGDBAowbNw7Dhw/HY489ho8//hjJZBJFRUWq/y0uLjYs1Lv11lvR2Ngo/2zbti0bm08QBEEQBEEQRIDwPc9ZXV2NZDKJ/v37o7a2VvVYc3MzCgsLdZ9XVFSEyspK1Q9BEARBEARBGDFz5kyEQqGUn88++yzj1968eXNaEq50n+cUvc8+ZswYz983H8h6QHTjjTfijTfekP9euHAhwuEwDj30UCxYsEC+f/Pmzejs7ETPnj2zvYkEQRAEQRBEALn88stRX1+PefPmAQDq6+tRX1+PE088MePXHjJkiGGphxfPS4cxY8bIn7m+vh5ff/11Vt4318m67fbhhx+O2267Df3790c8Hsf06dMxZcoUnHXWWWhsbMTLL7+MyZMn4/7778cZZ5zhSrMlgiAIgiAIwlsEQUB7lz8NWksKIrayLIWFhSgsLERFRQUAUankFuFwOK3XS/d56RCJRLL2XvlE1gOiyZMnY9WqVbjwwgtRUVGBH//4x7jvvvsQjUbxzDPP4PLLL8fNN9+MRCKBzz//PNubRxAEQRAEQaRBe1cCh/zhI1/ee+U9Z6O0MPNp7ZQpUzBs2DCMHDkS9957L6ZPn45f/epXAID58+dj+vTpWLt2LUaPHo0XX3xRJTlj9s98R5vPPvsMU6ZMwaOPPorp06ejqakJd999N379619n9LylS5fi8ssvx65duzB58mS8//77uP766zF9+vS0P/uwYcPw3HPPYe7cuXjhhRfw0Ucf4bDDDrN87B//+Aduu+021NXVYeLEiXjooYdkC2yz5+USvtQQzZgxA/X19di6dSv++te/oqysDAAwYcIErFu3Ds8884zsRkcQBEEQBEEQ2eKjjz7CE088gYcffhgTJkwAIDaivfjii3HJJZdg48aNOP7443HzzTfber26ujrcf//9eO+993D33Xfj5ptvRnt7e0bPmzZtGiZNmoS5c+fi+eefx4svvoif/exnlq+5bNkyVFdXyz//93//p3r8jjvuwM6dO/H6669j//33t3xs0aJFuOKKK/DAAw/giy++wKJFi3DLLbfYfs1cIesZIisGDhyIgQMH+r0ZBEEQBEEQhANKCiJYec/Zvr23W2zcuBFr165FVVWV6v4lS5agqqoKS5cuRXNzM9auXWvr9VpaWvDkk09izJgxOPDAA3HDDTdgz549GDp0aNrP+/777zFz5kwceOCBOOSQQ7B582b84Ac/sNyWUaNG4f3335f/1srnqqqqMHPmTN3n6j327LPPYtKkSXLg+PDDD+OMM87AX/7yF1nCaPaauULOBUQEQRAEQeQoHY3Avk3AgMP93hIiBwmFQq7I1vxm8uTJKcFQOBzGww8/jOeeew4jRozA0KFDkUjYq5fq0aOHLBNj7sm8PC6d540cORJff/01evfujXXr1uGQQw6xtS2FhYUYNmyY4eNmkju9x7Zt24aTTz5Z/nvEiBFob2/H3r170adPH8vXzBV8t90mCIIgCCJPePs64JlTgJolfm8JQXgGK+Xg+eyzz/Dkk09i1apVWLRoEX75y1/afr10W8OYPW/06NH49a9/jf79+2Py5Mmu1eXofXazx4YMGYKNGzfKf2/YsAGlpaXo3bu3rdfMFfI/jCcIgiAIIjs0Sk3QG3cA++VeYTRBeEVLSwsAoLGxEevXr8eNN95oK8vjBRs3bsS8efPw5ZdfoqqqCoMHD7b93EQigYaGBtV9VVVVafdBuuqqq3DKKafg3HPPxcEHH4ybbroJ11xzTVb6KrkJZYgIgiAIgrBHIi7eJuP+bgdBZJnx48fjRz/6EY488khMmzYNV199NXbu3Indu3dnfVuGDRuGfv364ZRTTsHw4cNRUlKCadOm2Xru8uXL0aNHD9XPjh070t6Wo48+Gi+99BJ+97vf4YQTTsBRRx2FGTNmpP16fhES/ApvXaapqQlVVVVobGxMOzVJEARBEIQJjx0D7F0DXDITGP1jv7eG8JGOjg5s2rQJw4cPly2Wiezw/PPP44033sBzzz2H0tJSLFmyBOeeey727NnT7ebAZsehk9iAJHMEQRAEQdgj2SXd+tN8kyAI4Ic//CFee+01jBkzBu3t7Rg+fDj+/Oc/d7tgyE0oICIIgiAIwh5MKkcBEUH4xvDhwzFnzhy/NyNQUA0RQRAEQRD2YDVEAgVEBEEEBwqICIIgCIKwB2WICIIIIBQQEQRBEARhD7mGiFzmCIIIDhQQEQRBEARhD5YZIskcQRABggIigiAIgiDskWAZoqS/20EQBOEiFBARBEEQBGGPJDVmJQgieFBARBAEQRCENYKg1BCRZI7IU04++WT8/ve/V913xBFH4I9//KOt52/evBmhUMjxY07+xy5uvpYZn332GUKhkOqnvLzc8/fNJhQQEQRBEARhDe8sRy5zRJ5y1lln4fPPP5f/bmxsxNKlS3HmmWdm/NpDhgxBfX19xq/Dc+qpp2LmzJlZez8jKisrUV9fL//s2LEjK++bLagxK0EQBEEQ1vAyOZLMEXoIAtDV5s97F5QCNrIlZ511Fu699160t7ejpKQEX3zxBaqqqnD00UdnvAnhcBjV1dUZv04uvl8oFMrqZ8s2lCEiCIIgCMIaJpcDAIFMFQgdutqA+wb482MzEDv66KNRXl6OBQsWAADmz5+PH/7wh4hEIvLfhx9+OEpLSzFu3DgsX77c9sc3krDNnj0bI0eORK9evVKyPUbvN23aNIRCIXz++ee48sorEQqFMG3aNFvvt3z5cpx44omoqqrCueeei+3btwMQpW/Dhg3Dv//9bwwdOhQ9evTAo48+avvzGcGyWA8//DCGDh2Kf//737YemzdvHg4//HD06NEDl19+ORoaGmw9zwsoICIIgiAIwhpVhogkc0R+Eg6H8cMf/hDz5s0DIE7KzzrrLABAMpnExRdfjEsuuQQbN27E8ccfj5tvvjmj99u9ezcuu+wy/O53v8M333yD999/X37M7P3+8pe/oL6+HieccAIef/xx1NfX4y9/+Yvl+7W0tOCss87CmWeeiaVLl2Lw4MG48MILkZScIevq6nD//ffjvffew913342bb74Z7e3tlq/b2NiI6upq+Wfq1Kmqx59++mnMnTsXzz77LI4//njLx7Zt24Zzzz0X119/PRYvXoyWlhZMmTLF9mu6DUnmCIIgCIKwJkGSOcKCglLg9zv9e2+bnHnmmZg1axba29uxePFivPbaa/JjS5YsQVVVFZYuXYrm5masXbs2o8368MMPMWLECFx99dUAgLvvvhvnnnuu5fuVlJSgpKQE0WgUpaWltuVq7777LioqKnDnnXcCAB599FH06dMH3377LQAxYHryyScxZswYHHjggbjhhhuwZ88eDB061PR1Kyoq8P3338t/a00VWlpaMG/ePBQUFKQ8V++xv/3tbzj++OPl/fLUU09h4MCB2LVrF/r372/5mm5DARFBEARBENbwQRC5zBF6hEJAYZnfW2HJWWedhd/85jeYN28ehgwZgmHDhgEQs0cPP/wwnnvuOYwYMQJDhw5FIpHZsV5TU4PBgwfLf48YMUL+3Yv327ZtG4YPHy7/XVRUhAEDBmDbtm3o06cPevTogcMOOwwAUFhYCAAQBMHydcPhsLyf9Jg2bZph4KL32LZt21T7YsCAASgqKsK2bdvkgMjsNd2GJHMEQRAEQVjD1xCRZI7IY4YNG4ZBgwbhT3/6kyyXA8QamyeffBKrVq3CokWL8Mtf/jLj9+rbty927lSyZlu3bnX0fuFw2FbAwhgyZAg2bdok/93R0YGdO3diyJAhAES3OC8oKzMOhPUeGzJkCDZu3Cj/vWPHDnR2dsrbafWabkMBEUEQBEEQ1lANEREgzjzzTMyZM0dlt93S0gJArJf58ssvceONNzoKRvQ4++yzsWbNGrz00kvYsGED7rrrLkfvN3LkSHzyySeoqanBJ598YplBOv/889Hc3Iy7774bW7ZswQ033IADDjgA48aNy+hzCIKAhoYG1U8m2ayf/exn+Oqrr/Dss89i06ZNuPbaazFhwgT069cvo+1MFwqICIIgCIKwJkGSOSI4nHXWWYhGo/jhD38o3zd+/Hj86Ec/wpFHHolp06bh6quvxs6dO7F79+6032fgwIF47bXXcPfdd+PEE0/ECSec4Oj97rjjDmzZsgXDhw/HtddeK5sjGFFeXo6PPvoIH3/8MQ499FBs3boV77zzDsLhzKb8TU1N6NGjh+rn66+/Tvv1Bg0ahPfeew+PP/44jjjiCJSVleHFF1/MaBszISRkGvrmCE1NTaiqqkJjY6Nn6UCCIAiC6LbsXgk8+QPx93FXA+c95O/2EL7S0dGBTZs2Yfjw4SguLvZ7c4huitlx6CQ2oAwRQRAEQRDWqGqIyGWOIIjgQAERQRAEQRDWkMscQRABhQIigiAIgiCsUfUhMq9jIAiCyCcoICIIgiAIwpokNWYlUglIKTqRp7h1/FFARBAEQRCENXwNEUnmuj2sYWZbW5vPW0J0Z2KxGAAgEolk9DpRNzaGIAiCIIiAQ32ICI5IJILq6mrs2bMHAFBaWopQKOTzVhHdiWQyidraWpSWliIazSykoYCIIAiCIAhrEiSZI9T0798fAOSgiCCyTTgcxpAhQzIOxikgIgiCIAjCGpXLHJkqEEAoFMJ+++2Hvn37oqury/oJBOEyhYWFGTedBSggIgiCIAjCDqo+RCSZIxQikUjGNRwE4SdkqkAQBEEQhDV8EESSOYIgAgQFRARBEARBWJMglzmCIIIJBUQEQRAEQVhDLnMEQQQUCogIgiAIgrCGaogIgggoFBARBEEQBGENHwSRZI4giABBARFBEARBENYkKENEEEQwoYCIIAiCIAhrktSYlSCIYEIBEUEQBEEQ1iTJZY4giGBCARFBEARBENao+hAl/dsOgiAIl6GAiCAIgiAIa1Q1RCSZIwgiOFBARBAEQRCENXwQRJI5giACBAVEBEEQBEFYQ32ICIIIKBQQEQRBEARhjaqGiCRzBEEEBwqICIIgiGDyxV+AWZOABE3eXYGvIRLIVIEgiOAQ9XsDCIIgCMITFjwFtOwCalcB/Q/1e2vyH1UfIpLMEQQRHHzPEI0fPx4zZ84EAEyfPh2hUEj+GTlypL8bRxAEQeQvrOaFz2wQ6ZMklzmCIIKJrwHRq6++io8++kj+e/HixXjvvfdQX1+P+vp6fPfddz5uHUEQBJHXsEk7ZTPcIUEucwRBBBPfJHP79u3DTTfdhFGjRgEA4vE4li9fjpNPPhnl5eV+bRZBEAQRFFjz0CRliFyBJHMEQQQU3zJEN910E3784x/juOOOAwAsXboUgiDg8MMPR0lJCcaPH4+tW7f6tXkEQRBEvsOyGCTvcockmSoQBBFMfAmIPv30U8yZMwcPPPCAfN+qVaswevRovP7661i5ciUKCgowdepUw9fo7OxEU1OT6ocgCIIgZGTJHAVErkC22wRBBJSsS+Y6OjowdepUPPnkk6isrJTvnzRpEiZNmiT//dhjj2HEiBFoampS/R9jxowZuPvuu7OyzQRBEEQewibwJO9yhwQ1ZiUIIphkPUN07733Yty4cTjvvPNM/6+6uhrJZBI1NTW6j996661obGyUf7Zt2+bF5hIEQRD5iCAokjlymXOHJJkqEAQRTLKeIXrttddQW1uL6upqAEBbWxveeOMNzJs3D+PHj8ell14KAFi4cCHC4TAGDx6s+zpFRUUoKirK1mYTBEEQ+QRf40LyLncg222CIAJK1gOi+fPnIx5XBtL/+Z//wXHHHYe+ffvitttuQ//+/RGPxzF9+nRMmTIFpaWl2d5EgiAIIt+hehf30crkkkkg7Hs7Q4IgiIzJekA0aNAg1d/l5eXo3bs3Jk+ejFWrVuHCCy9ERUUFfvzjH+O+++7L9uYRBEEQQYCXdFG9iztopYdCAjnQ350gCCJjfOtDxJg5c6b8+4wZMzBjxgz/NoYgCIIIBqqeOVRD5AraTFsyDkQK/NkWgiAIF6GlHYIgCCJ4kGTOfbSBJWXeCIIICBQQEQRBEMGDTBXcRxsAkdMcQRABgQIigiAIInioJHM0cXcFbQ0R7VeCIAICBUQEQRBE8OAn69SHyB1SaogoICIIIhhQQEQQBEEED4FqiFxHux9JMkcQRECggIggCIIIHirJHAVErqDnMkcQBBEAKCAiCIIggkeS+hC5DtUQEQQRUCggIgiCIIKHymWOaohcgSRzBEEEFAqICIIgiOBBkjn3IVMFgiACCgVEBEEQRPCgxqzuQwERQRABhQIigiAIIngIVEPkOtoaIpLMEQQRECggIgiCIIIHn82gPkSZIwhKABQuEG8p80YQRECggIggCIIIHkneVIEm7hnD78NosXQfZYgIgggGFBARBEEQwYMas7qLKiAqFG95Jz+CIIg8hgIigiAIInioXOYok5ExvOxQzhBRoEkQRDCggIggCIIIHiqXOaohyhhVhqhIuo8CTYIgggEFRARBEETwIMmcu/D7MMIkcxQQEQQRDCggIgiCIIIH9SFyF7YPwwVAOKq+jyAIIs+hgIggCIIIHknqQ+QqrIYoHAVC0tQhSaYKBEEEAwqICIIgiODBy7moD1HmsGxQpAAIR8TfSTJHEERAoICIIAiCCB4qlzmSdmWMLJmLkGSOIIjAQQERQRAEETyohshd+BqikJQhIikiQRABgQIigiAIInjwTUNp4p45fA0RSeYIgggYFBARBEEQwYMkc+7CgsoIb6pAARFBEMGAAiKCIAgieFBjVndJ8hkiVkNEARFBEMGAAiKCIAgieFBjVneRJXPkMkcQRPCggIggCIIIHirJHE3cM0Y2VYhypgoUaBIEEQwoICIIgiCCB7nMuYvch4gkcwRBBA8KiAiCIIjgwbvMUWPWzOEzRGFp6kCSOYIgAgIFRARBEETwIJc5d+FriKgPEUEQAYMCIoIgCCJ4qCRzNHHPGFWGiCRzBEEECwqICIIgiOBBLnPuoqohIpc5giCCBQVEBEEQRPBQSeaohihjyGWOIIgAQwERQRAEETySnKkCTdwzR68PEUnmCIIICBQQEQRBEMGD+hC5i5whinCSuaTx/xMEQeQRFBARBEEQwYNqiNxFriEqIMkcQRCBgwIigiAIInjwWSHqQ5Q55DJHEESAoYCIIAiCCB589kJIAILg37YEAb0aInKZIwgiIFBARBAEQQQPbX0LZTMyg68hCoXV9xEEQeQ5FBARBEEQwUMbANHkPTP4GiJZMkemCgRBBAMKiAiCIIjgoQ2AqBdRZqhqiEgyRxBEsKCAiCAIggge2sl6UDNEdRuA1jrv34evIQpRHyKCIIIFBUQEQRBE8EiRzAVw8t62D3jiOODlH3n/Xqo+RFH1fQRBEHkOBUQEQRBE8OgONURNO4FEDGjY5v17qWqIpKkDSeYIgggIFBARBEEQwUM7WQ9iL6JEp3ibjWCPryGSJXNkqkAQRDCggIggCIIIHt0hQ8SCvGwYRqj6EJFkjiCIYEEBEUEQBBE8UlzmAijvimczQyTtv3CEXOYIgggcFBARBEEQwaM7uMwlYuKtkPRevsayUBFymSMIInhQQEQQBEEED22AEMQ+RCwgArzP1uj1IQpikEkQRLeEAiKCIAgieKRI5gI4eWeSOcB70wi5hohvzEqmCgRBBIOo3xtAEARBEK6TIpkLoLyLzxB5HfCx/RcpABBS30cQBJHn+J4hGj9+PGbOnAkAWL58OcaNG4cePXrg5ptvhiAI/m4cQRAEkZ90B5c5PkPkeUDEZ4jIZY4giGDha0D06quv4qOPPgIAdHZ24oILLsBRRx2FRYsWYeXKlXKgRBAEQRCO6A6SuaxmiFgNUQG5zBEEETh8C4j27duHm266CaNGjQIAfPDBB2hsbMTDDz+M/fffH/fddx+ef/55w+d3dnaiqalJ9UMQBEEQAFLrWwLZmDWLAZFcQxQhlzmCIAKHbwHRTTfdhB//+Mc47rjjAABLlizBcccdh9LSUgDA2LFjsXLlSsPnz5gxA1VVVfLP4MGDs7LdBEEQRB6QIpkL4OQ9q5I5roYoTAERQRDBwpeA6NNPP8WcOXPwwAMPyPc1NTVh+PDh8t+hUAiRSAT19fW6r3HrrbeisbFR/tm2bZvn200QBEHkCd1CMsdlvbJaQ0SSOYIggkXWXeY6OjowdepUPPnkk6isrFQ2JBpFUVGR6n+Li4vR1taGHj16pLxOUVFRyv8TBEEQBIBu0piVt93OYg0RMzyiDBFBEAEh6xmie++9F+PGjcN5552nur9nz56ora1V3dfc3IzCwsJsbh5BEAQRBFIkcwGsIcqmZI4FXOEIucwRBBE4sp4heu2111BbW4vq6moAQFtbG9544w0MGzYMXV3KBWvz5s3o7OxEz549s72JBEEQRL7THWqIsiqZk14/UqAYVpBkjiCIgJD1gGj+/PmIx5WB+3/+539w3HHHYcqUKTjkkEPw8ssvY/Lkybj//vtxxhlnIBKJZHsTCYIgiHyHTdYjhaIbWxCzGbxkzusMGF9DFJJ+TyaN/58gCCKPyHpANGjQINXf5eXl6N27N3r37o1nnnkGl19+OW6++WYkEgl8/vnn2d48giAIIgiwjFC0OLgBUZy33fY4W6PqQ9Slvo8gCCLPyXpApIVvvjphwgSsW7cOixYtwvHHH48+ffr4t2EEQRBE/iJLvKQ6VOpDlOF78TVE5DJHEESw8D0g0jJw4EAMHDjQ780gCIIg8hk2WY9KbqRBzGYkstmHSAooIwVAnPoQEQQRLHxrzEoQBEEQnsHqW+SAKICTd14y53UGTJbMRclljiCIwEEBEUEQBBE82GQ9Wqz+O0gkslhDlOBriKSpg0CmCgRBBAMKiAiCIIjgwbvMAcHsQ5TNGqIkV0MUIskcQRDBggIigiAIInjwLnNAMDNEcR9styMFJJkjCCJwUEBEEARBBA85ICpU/x0kfMkQRclljiCIwEEBEUEQBBE8hG6QIcpWDVEyqdQLhQtIMkcQROCggIggCIIIHkmN7XYQ+xDFs2S7zb8234eIAiKCIAICBUQEQRBE8JAbswa5D1GX/u9uw9cnRQpIMkcQROCggIggCIIIHimNWQM4ec9WY1ZVhihKkjmCIAIHBUQEkUt0tvi9BQSR/wiCUvMSDXCGKJ6lGqIEHxBxGaIg7lOCILolFBARRK7w+YPAA0OBrd/4vSUEkd/wwYFsqhDAGqJElmy35cAnJDZlZbbbJJkjCCIgUEBEELnC9oXixGPXUr+3hCDyG36iLjdmDWA2I1u223wPIkCRzAGiAx1BEESeQwERQeQKXe3ibRDdsAgim+hmiAKWzUjEFVkgkJ0aIpYZCodTHyMIgshjKCAiiFyhq028DaK0hyCyCT9JD2oNES+XA7JTQxSWMkQsMAJINkcQRCCggIggcgU5QxQz/z+CIMzhJ+lB7UOkHSc8td1mAZEklVNJ5iggIggi/6GAiCByBZYhCtrEjSCyDV/XEtQaorgmIMpmDVGYD4gCtl8JguiWUEBEELkC1RARhDvwrmhyQBSwTEaKZC6bNUS8ZI5MFQiCyH8oICKIXCHGMkQkmSOIjGCSuXBEmbwHLZOhXTjx8vMlNAFRiDdVCFigSRBEt4QCIoLIBQSBM1UI2MSNILINm6SHo4rMK2hmJXEfM0ShkBIU0XhFEEQAoICIIHKBRJeyqk0ZIoLIDDZJD0WUepegZTKyKpnT1BAB1JyVIIhAQQERQeQCLDsEUEBEEJnC6lpIMucO2gwRoDjNBS3QJAiiW0IBEUHkAsxQAVD0+gRBpEeyG9QQaSVzXo4b2hoigMu8BWy/EgTRLaGAiCByAcoQEYR7qCRzBer7goKfLnOAEhCRyxxBEAGAAiKCyAX4DFHQir8JItuoXOakiXvQMq9Zlczp1BCRZI4giABBARFB5AIqyRwFRASREbzLXHeRzHm5kGKWIQrafiUIoltCARFB5AIkmSMI92ABUSjANUTaccLLTI1uDRG5zBEEERwoICKIXEAVEFGGiCAyQpbMhbtRQJSFGiKSzBFaWvcCb10DbP7C7y0hiIyIWv8LQRCeQwERQbiHqjFrQAOirDZmlcYkVYZIWk8lU4XuzZoPgKV/B9rrgWEn+r01BJE2lCEiiFxAVUNEkjmCyAiVy1xAAyJ5nAhJf2e7hiig+5VwRrxDvG3Z7e92EESGUEBEELkAucwRhHsI3aAPEQuICkrF22zXEJFkjgCU86q1zt/tIIgMcRwQDRs2DF9//bUX20IQ3ReSzBGEe3SLxqxSQFTIAqIs1xDJfYgoIOrWsOtVay0gCP5uC0FkgOOA6JhjjsEXX1DxHEG4CtluE4R76LnMBa4PkVRDJGeIvJTM6dUQBTTQJJzBjo1EJxBr8XdbCCIDHAdETzzxBGbPno0HH3wQ7e3t1k8gCMIayhARhHt0B8kcM1UoLBNvs5EhUknmpOlDkkwVujX8QkNrrX/bQRAZ4jgg6tu3L+bPn49bbrkF5eXliEQiCIfDiEQiXmwfQXQPyFSBINyDn8AHNSBiCyd+1RCRZI4A1OcV1REReYxj2+1NmzZ5sR0E0b3hM0RkqkAQmdEtGrOyDFE2aoikMUlVQxTQ/Uo4g79eUYaIyGMcB0RDhw71YjsIontDNUQE4R68ZE6exAtioBQOiJqBmSoUSJK5bNtuk8scAaiPu7a9/m0HQWRIWrbb//73v3Huuefi0EMPxXnnnYfZs2e7vV0E0b2IUQ0RQbiGymWOC4CClM2QbbdLxFuSzBF+kKQaIiIYOA6InnrqKVx99dU46aSTcPvtt+P444/HL3/5Szz99NNebB9BdA9UpgpUQ0QQGaEnmQMCFhD5bKoQpgwRAU1ARBkiIn9xLJl78MEH8eabb+Lkk0+W7zvxxBMxZcoUTJ061dWNI4huAy+ZExKic1OY+iYTRFroucwBwQqI5D5ELCDKgu02X0NEkjkCUCsaKCAi8hjHM66Ojg5UVVWp7quqqkJHR4drG0UQ3Y4ujYU9GSsQRPqoXOa4SXyQehGlSOaykSHi5IckmSMAkswRgcFxQHTVVVfhsssuw9///nd8++23mDVrFiZOnEjZIYLIBF4yB5BsjiAyQZbMhaVMa0i6P4ABkZwhykYNEbnMERr4759MFYg8xrFk7q677kJVVRXuvPNObNu2DUOHDsXUqVNxww03eLF9BNE90GaIyFiBINJHkJqFsixGOCpmXYM0eWeNWQv8asxKkjkCJJkjAoPjgCgcDuOmm27CTTfd5MX2EET3JCVDRAERQaSNdgIfxIBIK5nz1HZbrw+RJDAhyVz3JqkJiAQBCIX82x6CSBOq2iasibUCK94GOpv93pLgkpIhIskcQaQN7zIHKBP5IAZEWXWZ42uImGSOAqJuDV+Xl+wCOhr92xaCyADHAdGwYcPw9ddfe7EtRK6y8HngzSuABU/6vSVqGrYCi15UpCP5SjKhWOjK91GGiCDShneZ42+DFBDJkrlS6Q5BdKf0Ar0aIpLMEUDqOdVW5892EESGOA6IjjnmGHzxxRdebAuRq7TsFm+bdvi7HVrm3APM/g2wOs8bA/PZoWgW5C+E/ySTQAs5MnmGNqMRRAMANkYUlir3ebWQYtaHiCRz3RvtMUdOc0Se4jggeuKJJzB79mw8+OCDaG9vt34Ckf8waUas1d/t0MJWovK9kJOvHyqqEG9JMhds3v8f4KEDgJ3f+70lwYRlSkJBDog0pgqAd59Pt4YogPuUcI7Wyp4CIiJPcRwQ9e3bF/Pnz8ctt9yC8vJyRCIRhMNhRCIR6ydz1NXV4auvvsLevXk+me0OyAFRm/n/ZZt4jgZqTmEBUUEpECkUf6cMUbDZvQKAANSu8XtLgkmKZE6ayAepDxGTzKkyRF4FRJr9CYiW5vxjRPckJUNEczoiP3EcEG3atEn+2bhxIzZu3Cj/bpdZs2Zh5MiRuP766zFkyBDMmjULADB9+nSEQiH5Z+TIkU43j/ACNjnvyrHAgwVqWkOCfINtf0GJsgJLAVGwYav72toxwh1SXOYCWEPExogCPiDyKDhh76XqQ8Qkcx7VLRH5ATunyvqItxQQEXmKY9vtoUOHZvSGDQ0NmD59OubPn48xY8bglVdewe9+9ztMnDgRixcvxnvvvYfjjz8eABxnnQiPYCuRuZaJYZNJrWV1vqHKEDE3LAqIAg3Lbua7IUiuonWZC6K8S5bMlUBsPCt4t5CiW0MUwH1KOIcdcxX9Rbmc281ZF70ILHoeuPxNoHI/d1+bIDiybrvd3NyMRx55BGPGjAEAHHbYYaivr0c8Hsfy5ctx8skno7q6GtXV1aioqMj25hF65KpkTs5c5dh2OUWVIWKSOaohCjRyhoi+Z09IkcwFbPKeTCqfJVLk/efTqyEilzkCUI65igHirds1RMveBHYtA7Z86e7rEoSGrNtuDx48GJMmTQIAdHV14aGHHsJFF12EpUuXQhAEHH744SgpKcH48eOxdetWw9fp7OxEU1OT6ofwiFyVzLHV9cBI5kpJMtddYBkiCoi8QVvzEmEBQ0DOK/64iRZmISDSqSEilzn36GwBtnzlnW26l/AZIsB9yRw71uMd7r4uQWjwzXZ7yZIl6NevHz7++GM88sgjWLVqFUaPHo3XX38dK1euREFBAaZOnWr4/BkzZqCqqkr+GTx4cMbbRBiQ6xmiXJPyOYWXzIUpIOoWsAxRnAIiTzCUzAVk8s7XnkUKvW88a1ZDFJSsm598cifw4jnAmvf83hLnyBkiSc7mekAkHXsUEBEe45vt9tixYzFnzhyMHj0aV155JSZNmoQFCxZg3LhxGD58OB577DF8/PHHhpmfW2+9FY2NjfLPtm3b0t4WwoJctd1OBC1DRJK5bkOcTBU8JeiSOT6QjhR6H5zo1RDJkrk8zGrkGnUbxNvGHOv1Zwc5IJIyRG7XELHXp3pLwmMcmyr07dsXADB//nzceuutAABBEBAKhZBI2F99C4VCOOKIIzBz5kwMHToU9fX16NGjh/x4dXU1kskkampqUFlZmfL8oqIiFBUVOd18Ih1kN7c2QBCAUMjf7WHw25XPyBmiEmUiF5SJG6EPu7jTRd4bUlzmAhYQsUA6UiiOx37UEJFkzj06m8XbfFwgYRmcSlZDtFcMksMulajLGaI83DdEXuE4INq0aVNGbzh37lx88MEH+NOf/iRuQFTchHvuuQc/+MEPcOmllwIAFi5ciHA4TFK4XEDOVghiNoPve+En8YAERDFOMpeg2pJugWyqQNJITwi6yxwbH1hGOeyxZE63DxFJ5lyjU1LC5KOEln3/5f3EWyEBdDQApT3def0EOXIS2SHrttsHHXQQJkyYgAMOOADnnHMObr/9dpx11lk44ogjcNttt6F///6Ix+OYPn06pkyZgtLSHJl8d2f4QTrWmjsBURD7ELHgjgKi4JKIK71b8nFFOB+QJXPSKjULiILSmDWuDYg8/ny6NUQBq8vykw4pIMrHcT/J9cMqrgI6GsUskVsBkSyZoxoiwlts5TR//etfo7NTuXDPnz8fsZhy4ra0tODAAw+09YYDBgzAm2++iUceeQSjR49GW1sbXnnlFUyePBkXX3wxLrzwQkyZMgVnnXUWHn30UYcfh/AEfpDOFae5ZFIZiHPN7MEpKlOFgE3ciFT4ICgfV4TzATmjEXDJXFSSjftRQ8SCTZLMZQ7LEOXjAgm7VkWiQGlv8Xc3rbdzQTInCNRwthtgKyB6/PHHVQHRhRdeiF27dsl/JxIJbNiwwfabnn322Vi5ciWamprw5ptvok8fscPxjBkzUF9fj61bt+Kvf/0rysrKbL8m4SF8QJQrwYcqSMuRbUoXMlXoXvAX9nycAOUDgZfMaWp6PK8hYpNe6kPkOom4cg3LxwWSJJc9LBPncq4aKyRzwGVu3kPAn/YH1n/i3zYQnmMrIBIEwfRvIuDkYvCRi9uULnyGSO5DlIcXRsIe/HdL37M3pLjMBazehQXVESlDJNtue1STJmeI+D5EJJlzhU7OSTcfF0j47GGZFxmiHHCZ271Mul3h3zYQnmMrIAqFQghxzmKhXHEZI7KDKkPU4t928GgDonwO0lUZIo+Lown/iZNkznO0Eq+gnVfZlsyZ9SEiyVxmqAKiPBsPkkmlHjJSwAVEdS6+Rw5kiOJk7NAdsGWqIAgCbrnlFtnmuq2tDffcc49sh93RQcVugYZ3wspFyRyQW+53TmEZosJSksx1B1QZIrrAeoIsmdOYKgQmIDKSzHkUnJj2IQrIPvWLDi4gyrcFEj4jGQ5wDZHcSJvmukHGVkB0xRVXoK2tDW1t4sTtpz/9KRKJBOrr6+X/mTx5sjdbSPhPLsrTtINjXgdELENUqqzAkh1zcOEvqvk2AcoX2Kp1YBuzaiRzXtpuJxMApAy8Xh8iksxlButBBOTfAgl/nYp4UEMkCJQhIrKGrYDoxRdf9Ho7iFxFENSDQM5I5jQBQ1cbgF6+bErG6EnmKCAKLnHKEHlOSmPWgGUz2CJVVGu77cG4we8zVQ0Rk8wl3X/P7oRKMpdn477q2OBriFwKiPhg29cMEQVE3QGXWgkTgYVfHQRySDKnzRDlyHalg8pUgSRzgYc/dul79oYUlzm20BCwgEjuQ+RhwMdP0sN6LnMB2ad+oZLM5dmEWxUQFbhvqsBL8vzMEJFkrltAARFhTkqtTo70IUrZriAERCXeu0UR/kOmCt6T4jIXdMmchzVE/D6LUGNW18lnUwUWLIfCYl8qJplzK0OUK/WWJJnrFlBARJijHYRyJUOknUjmynalA0nmuhe5cpEPMsmAB0RayZyXCylaWZT8O7nMuUI+B0RJjfsgM1Voq3MnUOYzujkhmaMMUZChgIgwRzsxj+Vqhqjdn+1wA14yR6YKwYcyRN6TIpkLmLwrRTLnYcDHXjMUAfiWGySZc4cgSObY8VfK6ngFoL1e9ynOXj9XJHOUIeoOUEBEmJM3krkc2a50UGWIqIYo8FBjVu/RSuaC1odIlsxlsYYorPFgkt+TTBUyIp8zRCyDE4kqtyU9xN/dqCPiFwb9DEbiVEPUHaCAiDBHOwjlijQtKBkiQdCYKlCGKPDEyVTBc1Jc5oIqmdPabntRQ6TpecQgyZw7dORxQKSVzAHu1hHlTIaIBUSUIQoyFBAR5uSqZC6lD1GOBGpO4Qd5MlXoHqj6ENEF1hNSJHMBDYi0kjlPbLc12TYGSebcge9DlG8SWvbd88Gym81Zc6aGKAd6IRGeY6sP0S9+8QtbL/bCCy9ktDFEDpKrbm4pgVqObJdT+MwW2W53D7SmCoKgrs0gMkeWzElrfkGrIYpnsYYooZMFUL0nZYgyQiWZy7MFkoQmEwso1tttdZm/vjZD5NdYyYIxui4HGlsZoqFDh8o/giDgtddeQ1tbG/r3749YLIZZs2ahszPPTmTCHrmaIUrpQ5SnkjkWYEaKxEmbvNIbkIkbkYp2pTMok/RcQs5qMMlc0PoQSceQLJnzMODTyg8ZJJlzh0BI5nQCIrdriAB/9k8yoRzjlCEKNLYyRHfeeaf8+1FHHYV//OMfOP/88+X7PvzwQ9xxxx3ubx3hP7naADUopgq8oQJAGaLugPa7jXem1mcQmdFtJHMF6ltPAiKDGqKQtJ5KGaLM4DNE+SaZS+gcG3INkQsBkfZ4jncoiwDZgh+vSeIcaBzXEG3atAnDhg1T3Tdo0CBs3LjRrW0icgnt5C1XMkTaC0e+Z4gKSsVbCoiCj/aiSt+1+wS+MSsLiLSNWb0IiAxqiEgy5w75LJmTs4d6NUQumCpoM0R+BCSqNgmUIQoytjJEPD/72c9w4YUX4le/+hUGDx6MnTt34vHHH8dPf/pTL7aP8Bs2IEWLxcEgVwKilIaxObJdTmG1T3KGKGATNyIV7bFLq47uk+IyF7AaohTJnB81RCSZyxhBUJsqJOOijXk4T/yu5POMC5bL3AyItNl0HwISyhB1GxwHRA8//DBGjhyJt99+GzU1Nejbty+mTp2K6dOne7F9hN+wAaC4GmjZlUOSOc3KUaYZopXvACveBn70/4Ci8sxeywmUIep+aLOb9F27D+uNEwpqHyIfGrNqa4jIZS5zYi2AoOnjlIgB4WJ/tscpupI5Zqrgsu02QBkiwlMcB0TRaBS//vWv8etf/9qL7SFyDTZZK+khBkTxDlEioZVPZBu5MWGRuFqaaUD05V+BHYuBQy8BDjo38+2zC9UQdT+0GSL6rt0nxWUuYJlXQ9ttL2uItKYKJJnLGN5QgZHoBAryJCAy7UPksu024E9AxI/Pybi4TdpzgQgEaeVlX3vtNUycOBEnnHAC1q1bh0svvRR797qwGkDkHmwFiHWfBnJDniYHatXibaamCp0t0m2z+f+5jTYgIpe54KO9qJMMw30C35g1i5I5rWMfgwWb2gwHYR92vSmuVu7LJ2MFvT5ELCBqr8/8OpYLGSLtglW+1XkRtnEcEN1222245ZZbMGLECCxZsgRhaVCcOnWq6xtH5ADs5C+qACD5/+eCbI4NUuxCkmmGiD0/1pLZ6zh+X5LMdTtSTBXoAus6QXeZy4U+RLJkjjJEacMMFYqrOGv4PBr7Ezo1RCU9IM8VMu1FlGKq4INkza8FrOZd3jRaJgxxHBA9++yz+PDDD3HfffehoKAABQUFuO+++/DJJ594sX2E37DBOVoIFEq1NbmYIcq0MSsLTLL92VIkc6zWgQbCwJKy4thNvuuORuClC4AFT3n/Xikuc9JtUDKvWsmcbMbiwbFk2IcoYEGmHzDJXHGlku3LpwUSPclcOAKU9hJ/z7SOSM92O9votUnwmr3rgYcPBv55lffvRcg4Doiqq6uxdetW1X11dXXo16+faxtF5BBy0WQRUChlMXIhIIprM0T5GhBJ78v2LQuIusskuTvSXSVz2xcCm+YBc//o/Wc2aswalMk7v1AFeFvPI8uiqDGr63Q2irdFVcrYn0+SOT1TBcC95qy5YLvth9PdnpWiFHXPKu/fi5BxHBDdfvvtmDBhAi6//HJ0dnbikUceweWXX06NWYMKvxLJZF25JJmTa4gykMwJAhcQZVsyxzJEJJnrNnRXU4UuaSIRawY2zff2vWTJXEBNFXhTGcBnlzkKiNKG1RAVVSjfZT6NB3q22wBnrJBphqibSuY6pEA5F+Za3QjHAdHkyZPxn//8B2VlZTj11FPR0tKCl156CT//+c+92D7Cb2StekGOSeY4O3AgM1MFfpD1K0MkmypwGSJByO62ENlBuwLcXTJE/Hm2+l1v3yvojVlTXOY8zIBZ9SGigCh9VJK5PFwM02vMCigmTG37Mnv97pohYgFRLsy1uhFpeQeedNJJOOmkk9zeFiIX4S+8uSSZk93vqsXbTDJE/HN9C4g0kjkI4kSD7D2DR3fNEPGTmdXvA+c97J19f+Abs2olc6xGKps1RCSZyxhmqlBUqQS3+bRAYiSZi0q24ZmObblgquBHDRFliHzBcYZo2bJlaGujL6nbIF94i3JLMhfXZIgSsfQLpvkgKFdMFQAyVggqLEPE5FzdJiDiJjOte8SaIq/QuswFrjGrkWQuizVEJJnLHD5DlNeSOU1AFHHJMS8XbLdTMvpZzBCxvo9EVnAcEJ199tlYuNDDCxmRWyR4yVyZ+HtOZIg0NURA+oGaKkOUI7bbQH5dGAn7sAtqUYX0dx6tCGeC9nOunu3N+ySTACS5aWAlc5qVeS8DPnKZ8w6+hiivJXOaTK9bx2NOZIj8qCFqUH7PhQXoboLjgGjKlCl46aWXvNgWIhdRSeZyMCAqLFdW2tMOiLjn+Z0h4lfayGkumLBjt6hS/XfQYZMZVl+warY3dXK8hCuwAZFRY1YPxgyrGiKSzKVPUCVzbpkDpdhu+5Eh8qFvHMsQAZm3FCFs4zggOv3007F69Wqcd955+OCDDzBv3jz5hwggue4y54aUz9caIm1AFFakKBQQBRN2gWUZou4SELHPecDZojyofpNoL+s2vMQkpOlDFAT5iSBw4zILiDz8fFYuc4CUlSMcwya+xVX56TCq14eI/zvTa5j2+X70aPLD2IEPiDIxjCIc4bhi+6qrxEZRNTU1uO666+T7Q6EQNm7c6N6WEblBPEczRHFNoBZrSd9YocvPGiKNZA4QP1O8Pb8ujIR92EWduTbm04pwJrAMUWlPYP8fAms/ELNE/Ua7+z66GaIA9ffiPwNbmc+G7bZRHyL2P+FCEA7hM0TRPK4h0h4bbvXTy4UaohTJXBZriADKEGURxwHRpk2bvNgOIlfRk8zlUoYoUqhkV9IdOHytIdJkiADxYhJvD468h1DDgvnuliGKczKvg84TA6LVs4FTf+fu+/DnjewyFyDJHD9BkyVzftQQcQERyebSQ9WHKB8lcwbHhlumClqjJF/6EPloqgDkxnyrm+BYMkd0M3hTBZbFyIUMEa+hz2vJHMsQlSn3uXUxIXKThEYyl08ToExgE4loMTDqHLH2b9dSoH6Lu++jK5nz0IUt2/ATNLkPkfT5vMiAGdUQqSRzAdivfqBymQuQZI59lkxr2uTXl47vbmOqwGeIcmC+1U1wnCFqbW3FE088gbVr1yKREAdBQRDw/fff47vvvnN9AwmfYRfDaBEgSDrxXDhB+WLOwkwDIu55iU7xtbVFol6hmyHKwwsjYY9EXDmPunOGqKw3MOR4YMsXwOr3gB9cZ/5cJyT1JHMB6kPEjpdQROfzeVlDpHES47MCQdivfpDvkjlDUwWXa4gKy0Xnte7QmDURVytVKEOUNRxniH7+85/j7bffxooVK7Bhwwb06dMH//znP3Haaad5sX2E37DVkVwzVeD7cGS6XVqpXTYDPr2ASK53oElG4OBXG3PBZS6bxxifIQKAg88Xb92232byrVAYCIXE32Ub4CDUEGkc5oDs2G5rJ70qyRyZKjimq0M594vz1GWOBeApckqXa4jkestckMx5/P2wIJlBNURZw3FANGfOHLz55pu4++67UVBQgAceeABPPfUUVqxY4cX2EX6jysTkkKmCrpTPhQwRkL3PJwjKe6lMFUgyF1j4i6nfkrmFzwH3Dwa2fJ2d94trJvKjzhVvt34NtO517320TVmBYNUQ8YYyDC9ttw1d5rjpA0nmnMPqhwBxwp+PygCtpI3h1mdh848iHw1osm2qwPcgAshlLos4DoiqqqqwZs0aHHvssfjuu++QSCRw2mmn4euvs3RRJbILb++aiwFRtEjJrqTtMqd5XrY+X6JLWc0myVz3gF3QQ2HlO/fre940T1wM2PpVdt5PmyHqMRToP1bMLqz5wL330ZN4sQmbkMx/i+iEWUDkQcBnWEMUUoLOIASa2YZlAgorxGM1UJI5l2raEjmQIZK/DynbrM0YuQ1fPwRQhiiLOK4h+v3vf4+zzz4bu3btwkknnYRTTz0VgiBgzJgxXmwf4TdxXjLHAg+fT1BVHw4X3O9SAqIsOc3x26vKEHm42kv4S4KTevo9AWIX2s4sHe/aDBEADDtJNFaoXe3e+wg6Mp4UR7Q89hPSk8z5UUPE7kskyGUuHeQeRJJ0VpbM5VFAZJUhcstUwc9suuwKWgl0NmYhQ6QJiChDlDUcXxWmTZuG//73vygrK8PMmTNx+umn4wc/+AHeeOMNL7aP8BtdyZzPAZGqD4cLgZp2wMlWhogFYqGIeoVNzhBRQBQ42MU1Wuh/zQA7/njpjpfwdX8MNqa4uQ9YBkglmePOr3w/r3Qlcz7UEAFchogCIsfwhgoAN+7nYQ1RSobILcmcdOwV+ZkhyrIrKGWIfMNxhgiAnA0qKirCXXfd5eb2ELkGL03LFckcf8FwJSDySTLHN2Vlxd9AsJpIEmr4DJHf0ki2EJCtjKhWMgeIgSHg7iRQzmhw631BckQzk8x5MWYY1RDx9+X7PvUDvgcRwJ0LeZQhShhkiOTjMcPjQjZVyIEMUXEl0AQfMkQUEGULxwHR8OHDEeInbxwbN27MeIOIHEPPvMDvFC5/0Y8WKT18XDNVyJZkTsdhDiBThSAT5xYYmOTJrwyRLJnLcoaIl3qx4MjNfaArmQtgQBT1uYYIUIJOcplzDt+DCFAyp3kpmfMqQ5QLpgqcZC4b25CSISLJXLZwHBDNnDlT/r2trQ0LFy7Ec889h/vvv9/N7SJyBb1anWRcHLT5C3I2kQvTpT4cmZoq+GW7bRgQkWQusPD1H35/z13ZDoh0MkQRD4JCXZe5ADUR1ZMeynWHWa4hIslc+gRKMqetIXJpUY8de4U5EBCxwDVrGaIQAIEyRFnEcUB0yimnqP4+55xzMHHiREydOhWXX365axuW17TtE21ki6uAin5+b43C6veBxS8CP3rM/nbpBUSAmEWJ9nR/G51uE8CZKqQZyLDAJBwVB+BsS+b4/QoEq2cKoSauZ6rgVw1RLmSIvAiIdCbwoZByfuf7eaVrqpAF2229GiKSzKWPNkMUzcOFMKPsoVt9sVIyRH70IfKphqisD9C6h2qIsogrVjvDhg3D5s2b3XipYDDvIeDxccCCJ/zeEjVfPQqs+xhY/x/7z5FNFQrFQY4NfH6uWmglIxnbbkufpayPeOt7hogkc4FFDgoKle/ZL4kMu9D6WkPkQVDI5FvajEZQJu96VsdefjbTGiJpH5PLnHPkDJE00fYiW+o1XvchyoUaItlUIcsZosr9xFu/SxS6EY4zRFdeeaWqhiiRSGDhwoUYNWqUqxuW12Q6QfeKug3irZPt4m23AaCwVDxh/Vy10GaI5NqmDPsQlfUGmmuyNwDxpgo8sqlCnk/ciFRUpgo+ZoiSSSCeZZc53qCF4YXTnp5kDghOQKQnmeM/myCoTVoyxayGKBuSucbtwOwbgeOuBfY/zbv3yTZyQFQl3uajZM6oD5FbxkA54TInfQaWyfP6+2EBUcUAoGYJZYiyiOOAaNiwYaq/Q6EQTjnlFEycONGtbcp/ClihcA4FRB1NYvoVsD+oCIKyQiMHROXiCevnqoV2QsACinQzO3KGqG9mr5Pu+xrWEOVIhiiZECfNJdV+b0n+w9tu+9mHiM/wZqMPkSAYZIg8MFUwqnnxsldPNjEzVQDEDJk2GMwEWTKnlyGSRCZe7tNlbwLrPhLPlyAFREaSubwyVdAxMAE4lUOmAZG0L1gNkZAQgyS9Y9Er/JLMyRmiHA+IateKqqNxV6kXu/IQx0fVnXfe6cV2BItMMxZeUL9J+d1uQKRyc9NkY/x0PtGuSpFkzlvemAysnwPc8D1Q0d/vrclv9Gy3/ZgA8edKrFnMGIU9bFaajCtSNn4i74Xttp7LHBAcO3sz221A/Hx6BgjpYsd220vJXN168TZb0s5skWKq4HOj5nRIGmSIXDNVYJI5rs423gFEyjN7XSekuMxlSTJXMUC8zXWXuTl3A6tnAxX7AWMu8ntrMsJxQHTaaacZ2m4z5s6dm/YGBQK26plLARGTywFAl92ASNPvBxAlc4DPkjlNUbFbpgplvcXbrNluG0jmnBak1m8GPp0BnPBroN9o1zZPZuf3YrZz71oKiDKFNxbwUyKjPVe6WpUVUC/gJxGeZ4i6sWQOcP/z+S2Zq5PaeQRNOqTtQ5RrC2F2MOpDxMa2TE0+ZMkcNzbFOxUJXTbwy3Y7XzJEbXXibeM2f7fDBRwHRAcccADefvttXH311Rg6dCg2bNiAF154AT/96U9x1FFHebGN+Ucu1hDt4wKidDJEvGQO8Fcyx/dGAjLb34KQgxkih5K5pW8AS2eJ0otz/+Te9jHkBp45vlKVD/Cr+7LDmg8TIO3ksrPZ44CIX1wpSv3di4BIm/EKSkCkJ5njV+jd/nx2TBW83Kf7WEAUsPEnRTKXj6YK7NgwyhBlGBCxgCpaJL5Hsiv7dUTs+8i27XaFFBDl+kIAmz+11vq7HS7gOCD68ssv8e677+LYY4+V75swYQKuueYaPProo7Zfp66uDmvWrMGBBx6I3r17O92M3IZNcP0oADRiHy+Zszngsgsv6/cD5IZkjk0gU2qI0hg4+CCq3K8aIoMMkd2AiG2vVwOn7EYWsAmJH8gZomJ/a8W0q45e1xGxsTBSqA5UvJgEGkrmAlZDxEvm+GyYVwGRbg2Rx5K5zhagZZf4e9DctgIhmTM4NlwzVeCyk9FiIOZDQCRniLJQQ5SIKwqVSkky19XmvlGKm7D5QUv+B0SOReO1tbVIJtVdqZPJJPbs2WP7NWbNmoWRI0fi+uuvx5AhQzBr1iwAwPLlyzFu3Dj06NEDN998MwRBcLp5uUGUZSxyKLLnJXN2zR60DnNAjkjmjFzmpIHDCXxAVJptyZxBhsipyxy7aHghvUomlNfNpeM5X+FX93nL6WyPdSkBkcdOc3wgyOOF7balZC7Pa4j0xuVwGAgxg4MsZojk90ymPuYGLDsEWF9z2vYBDXkk2zHsQ5RHAZGVZC4Ry2xs42uU/MqgyaYKXIbIq/FabsoKTp4u5JbaSAvbtgBkiBwHRNdeey0uvPBC3HjjjXjooYdwww03YMKECfjVr35l6/kNDQ2YPn065s+fj++++w5PP/00fve736GzsxMXXHABjjrqKCxatAgrV67EzJkznW5ebiBLuHIpQ8QHRHYzRBqHOQAoyLBexw20khEWpEFwPliyiWGkSLkw+Z4hcnhhZP/nxYWCnzjneuo+H5AzJUVqmVO2C/2132XM64CIOcxpXIg8sd02cJlzq1mkG6z/BNj8RXrP1bMvB7yTBJrVEHktmeOvW1YLMs+fBTx+jBJo5DLJhHINlTNEHpwLXpM0ODbksU3ILCOb4IJxtpiSzZrLZELJfrL5AeBd0NrRIN4WVijHBZDbi5GyZM5+UiRXcRwQ3X333XjyySdRV1eHOXPmoLm5Gc8//zzuuOMOW89vbm7GI488gjFjxgAADjvsMNTX1+ODDz5AY2MjHn74Yey///6477778PzzzzvdvNygIMcyRB1N6ujddg2RplYHUAwMfHWZ02SIolyGxek+57M02f5s8ntrAyKHK9nsAuHFpJqfOAdNsuIHqsas3IQ228YK2u/StwyRB6YKhpK5HKkh6mwBXpsIvHpJeotmepI5QJmUup4hMtif/H1eSeZ4ZUOs1XhlPpkU3ei62oCmnd5si5t0ckGbNiDKJxfEhIFkTlXTlsHn8TtDxAc+fIDilWyPZYiKq8TFBjY+5rJcnc1jAiCZS8vM/Sc/+Ql+8pOfoKurCx0dHaiosF+MO3jwYEyaNAkA0NXVhYceeggXXXQRlixZguOOOw6lpeLkcOzYsVi5cqXh63R2dqKzUzkxmppyaFUo12qIeNkB4MBlTmclMhckc1rJSCQq/p6ISQFRT/uvxSaGhWU+BkRGpgp2AyIPJXP8xJkyRJkjT2aL1OdVPAZks4VDiqmC1zVEOjIvQNkHTvuLxGPAwmeBkWcCfQ5UPyZL5rSmCjlSQ9TZLE70kl3iJL7/GGfPjxsFRNK+c7uhszwp1ZPMebxP+dpXISGeP3q9TuLtAKRgKVuNhjOBZbGixYrSwQv5qNcYmipwx2YilnqNs4u2hgjI7ryKD74KOWc7r4xw+IAIEBdL4x25s7iuhW/w3bbX+/YNHmNry5ubm3HTTTepZHGPPfYYqqurUV1djRNPPBFbt2519MZLlixBv3798PHHH+ORRx5BU1MThg8fLj8eCoUQiURQX1+v+/wZM2agqqpK/hk8eLCj9/cUuYYoR3SfvOwAyCxDlEuSOZWUL81ATZUhkga8WKt3mngeFnhlKpljg7YXgzS/P3N5lSpf4G23wxFlQpntugHt2OR5hkinKSugntw6mQiueQ/46PfAJ3elPiZnNAxqiPxegefH371rnD9f23aA4ZV8zU+XOe21y2gMUo1TeRAQaQ0VAPfqbrJJ0qCGiA+Q0g3QkwnIQW4uZIiiRZwrZhYyRAC3SJujARFfj56MK5K/PMVWQPTLX/4SX3/9NS688EIAwPvvv48bb7wRTzzxBDZv3oxRo0bhuuuuc/TGY8eOxZw5czB69GhceeWViEajKCpSD/DFxcVoa9M/EG699VY0NjbKP9u25VAxZa7ZbrMMUZF0kjl1mVOZKuTACaqXueKNFZwg1/FwkjkI9o0nMsHQVMHhxI3tD08yRCSZcxXtOeVXLyLtd+l5DZHBJJ6XDTqZ6NRvEW/1LsBWjVn9lszxn7N2rfPn6y1U8X/7UUMkeLSAVKcJiIzGd94IJx8yRNoeRIAmq5IHsrlkUvnetcdiOGxvsWf7ImOJI/88voYomxkifrwOhbyR+PLoZYiA3L32aue4eW6sYCsg+s9//oPXXnsNZ555JgDglltuwXXXXYcrrrgCgwcPxl133YUvvnBWIBoKhXDEEUdg5syZeOedd9CzZ0/U1qp3ZnNzMwoLC3WfX1RUhMrKStVPzsAmuMku9+UL6cAa2/U7RLy1O9nn5T0MWTKXC7bbfG1TugERV8cTLQEgWVtm4/O5LZnzYpCOkWTOVbS1NEwuk+1eRHp9iLzEKEMUiSoTJyfHb8tu4+fkemNWfkJXuzqN5+uMy4B3LnpGGTfAW8lcR5NSqM3GRKMxiB+n8iEg0jrMAelnS+3w0W3AB7e4+5r8eaSXPbRqH9GwFXjudGDWJP3HVX0QfcoQaZsgR7OdIcqBEgUztPOtlvw2VrAVEPXv3x9r1oip/eeffx7r16/H73//e/nx1atXo7q62tYbzp07FzfffLP8dzQqnkgHHXQQFixYIN+/efNmdHZ2omdPB/UguQI/wc1GpsEKJjvoywIimwOKXuCRU5I5PkOUppEFG2gKSsVVLTkDlgXrbas+RLlgqsCvAOWqjjmf0DokRnyqG8h6HyKDDBF/n5N90LzL+DlGLnO5UkPEj79708kQWUnmXP58fGG7Fi8lc0zZUNpbaYlgdN3Jt4DITDIHuLtAEmsFvn4M+OZJoLXOvddNagIWLezzGB0bTTXS7Q6D1+cDrgLvgxE9tNnYrGeIcmC+ZUZKhqgbBEQPPfQQLrvsMhx00EGYNm0a/vSnP6Fv377Yvn07fvGLX+Diiy/Gb37zG1tveNBBB+Hpp5/GM888g23btuGWW27BWWedhfPOOw+NjY14+eWXAQD3338/zjjjDEQiOqtSuQ6/CpoLsrl9mgyRU1OFnOtDpFOgLQ8cDve3NijJprGCZYbIru22l6YKVEPkKkYrjlmvIWLHvXS8+5UhAtKzG2YrkXoTR8GihiiXMkR1652rCBIGAYpXn8+0hshDlzm2kNdrf+vrTr5J5uSJLxcQeVVTyC92uDlh5Rfg0skQseuV0Xkvv35IclzzsYYoShkiXbQLa617/dkOl7Bl6XPeeedhxYoV+OabbzBy5EiMHTsWAJBIJFBZWYlZs2bhnHPOsfWGAwYMwJtvvonf/va3+J//+R+cffbZeOWVVxCNRvHMM8/g8ssvx80334xEIoHPP/88/U/mJ6GQKL+Kt/sfEPGW231Hi7d5b7stDZRRPiCSgopMTBWALAdEFhkiuxkfT00VuP1AGaLM0fbjifgsmSvvC9Rv8n4SadQ7B0hv1bXFLEMk1TVoJXNOM69ewX/ORAyo3wz0Hun8+SmSOQ9qiATB2EkM8FYyxxbyeu6vjNNGYxB/f2cOOc4aIdcQaaT+0SLxs7i5uMVnF1r2AH0Pdud1tRkcLWGL6xgb84wCJj4zqarfyabLnLbFh8fbYFhDlKPXXu38Ns8lc7ZttwcOHIiLLrpIdd/QoUPxyCOPOH7Ts88+W9dSe8KECVi3bh0WLVqE448/Hn369HH82jlDQXFuBERsla2sj/gDODdVUJkX5EAKV29C4IapApBlyZyRqYLDgChbpgqUIcqcXDNVqOgvBkRZa8yqkyGKppEhamY1RDqTKUPJXA5miADRac5JQCRL5oxst10M+PhAR6+GiFnserFPWe1rrxHiMQqYuMwFQDIHiJP/LrgsmePGcDeL3tl3HgrrWy1b1cJaNRTXmnn4IpnTqFHSGaucYOgyl6PXXu0CdJ6bKqTVh8hLBg4ciIEDB/q9GZlTUAq01/tfQ8SvshWw1Q2npgq5Jplz01RB+n828PDW216STCiDrexuJ5GuZM7rDFGuDsr5hLaWxi9TBRaMl/cVb7MmmdMxyXHagT7WqgRwes8xdJnLwRoiAKhdAxx0nv3ny5I5bUDkweezqhORJXMeuMyxxbyeI4CCr8XfDQOifJPM6ZgqAN7UFPKLsm6u4Ju5DwJK3yoryZyQEI9ZbcDNAi72Ol7X7+iRMl47HKuckncZomAFRPnbQSnXYSdOOp3I3YStsvUcwZ3MMXt9dvS06mzy7ucJqpu5StNUwS/JHH+RSqkhcriSLZsqeDCpVtlu5+ignE+kZIh8qiGSJXP9xNusmSro1RA5rA1gDnOAQYYoj1zmADEgcvR8A8mcF7bbVk5iXkrmmOV2z/2tF7yCkiHyoqaQV3O4WUNk1IOIIZsqGGWIuPv1zv2cyBBpFh+8rmPKuxqiYEnmKCDyilyJ7OXC1BHObT113dy4gMGv5nG6jVnT7I/kl6kCP5CkWBHnkqkCt525OijnEykrjn65zEnHd9YzRCYuc3YnGc1cQGTqMqe5vDmVonoF2xfsPHfanFXrVMjwwnZbVTifRZe5jkax8z0gmioUWIzL/NiUFwGRTh8iQAlqvZLMtbi4gp/QZHC0WJkqqGrp9M5jzYKsnCHK4uKR1tEx6zVEOVCiYAabP7FyDMoQEboU+FAAqAcvmYtymQg7tU3ySqSOZA6Cf/VR2kJHIP1muLLtNssQMcmcxyvmbIArKBULRnmcBkTsexKS7ve90poq5EsH9VxFm930YgJkBzlD1F/628cMkdOgkM8QJWKpxySTb6VI5nIlQyR9TtYGoXats/NKb1wGvPl8ljVEHrnMsetWWV8xaLDMEJFkzhB+n7maITIx2+DvN7om8dc3vfEvFzJE2tYjlCFSw+Zb1UPFWwqICF3SnaC7TR1nXeq0CaKeZI53RPMr+2VW25S2qYJPGSKtXA7giqPtSua4i4nbmQbV/vQxCA4KWrmTb32IWA2RJJmLtdiT0aaLWYbIqe02HxABqQsHhpK5XKkhkvZF30PEc72rFWjcbv/58richT5EvCxKu3ADiAX1br8noL5uAcr4bMtUIR9c5owkcw6bctuB3zduSprM+lMB1gt7Vtct+Tj3sYZIq5LxMkOU6FIWSvOmhkja3uoh0t9t3suvPYQCIq+I5kBAxMsOeo4Qb52c0Hq1OuGI8tmy4cSmh15jwrRd5lhgku2AyMByG0hDMhfT/90NtCtTZKyQGSkSDA8mQHbQSuYAb89n0wyRw4kOa8rKSAmIct1lTvqcReVi5h5wJpszdJnzsIbIqE7EqyBzH1f7CljXruZbDZGcIapS3++0ns4OqgyRB5I5wxoiB5I53VpAbYbIB9VNynjtYYaogwvkWaCc6y5zbP5U1keZF+ZxlogCIq/IhQwRW2VjsgPAmZRPtpzUrAD5ncbVzVylub8NTRW8lsyZZIis7Eq1WEkPMkGrXc5VLXO+oJV7ejEBsgM7d0t7KhMaLyeSpgGR0wyRZpVbe8wbuczlTB8iLlvW50Dx99q19p4rCCZ9iDyw3bZyEvNKMlfHOcwB3LhsMyDKdWmvnCHS1hB5YMPP75vWWvf2jaWpgkWAzh+nphmiLMnV9MhmH6KOBvG2sELJiuV8hkiaxxSWAuX5X0fkOCB66623MGzYMEQiEfknHA4jEtHRF3dn2ETXT9tttsrGZAeAwwyRgb1rgcVqndfo9iFKcyWFr+UBsme7bRoQOSiOTibVFxzXJXOa4zdXtcz5gCAYN2bNpmSOt3wvKMtO3ZzWTILHqZVtizZDpHmeLJnTmirkWIYoWgz0OUj8vXa1vecmEwCkCa12ocrLGiK9+iHAO5e5fQaSOaMFGf7+ZNz/2l0zBMGkMasHGWP+Op2IKRPvTJFtsa1qiCxstwH9IEf7+n5miLR94zzJEGnqhwD/F5+t4JUuATBWcNyH6Nprr8WUKVNw1VVXobBQp6cEIZILGSKt7ABwtsqiV6sD+J/G9SJDVJinkjnt/7idIdIOxH4FwXUbxJ+RZ+g3AcwHknEok1lto78smirw32FhqTgp62jwOEPEnNVcqCHiXeb0nmc0ic+1GqJoEVA9TPx9r80MET+J1AaXTu367WBVJ8L2qVemCkxS6CRDBIjHst5iUy4Qa1X2l5GpgpsT7pTmmXuBkh6Zv65lHyILV0eVssGJ7bYPNURRbYYoSwFRzrvMcSUHZZL8Oo+ttx0HRKFQCNdccw32339/63/uzuRCDZFWdgA42y49NzeAW7XwKyDSWW1O21TB5z5EuqYKnNRAEPSLmRnagMj1DJFmP2T7O9+7Dpj3J2DZm6J72OR3gBGnZncb3CKuM5n1ow+RPEEKiRf4IilDlBXJnEmGyC1TBcPGrLmYIWKSudXW5zr/XMBYMudLDZGL79neALTVib+za5eVdEgvIOLr43IJJpcLRVIXxKzqbtJBO4a37AF6H5D56xrV6jGspN/8IpAd2205WPTDZU5bQ+SFZC6fM0QlQFlv8fc8zhA5Xmr94x//iOnTp2Pv3r1ebE9wyKUMkUoy50KGyG9dq24fIrdc5rJku81eXzdDxK24WUknUgIijzJEbF9n6zuvXQP88yrg8WOApX9XrJSbarLz/l7AfzfyBdahgYYbaC3fWR1DNjJEmdpuJ+LKBZdN0o0yRCkucznWhyhaDPQ6AEAIaK8XV+6tkLc9lB3TCLlw3iALIEvmXHQoZHK58n5KsG61UKUdr3PZaY7vQaQNgL1ozJqSIXJpBd9KMmcV3FnabmuCcV8yREZ9iLKdIcrRgCjGzZ/YAkQeB0SOM0SvvvoqVqxYgaFDh+Lggw9GZaWS8p07d66rG5fXyOYFfgZEehmiNFzmUjJEWaqzMUK3DxELiDLtQ5SlDBFzymK2xzz850rEUt2keLyWzPGN15p2ZOc7X/cf4LVLlSBo1LlAcw2w87vcvTDYgV1EQxFF3uSlJt0IrUyUBUR+1RA52QettQAEsT6oYj+gcVv+usxFi8TvoHoI0LBFdJpjhclG8BM07WTa0wyRQRbAC8lcnUYuB9jIEHFZTwi57TRn1IMI8N5lDnCvOatdyZxRLSy/AOKoMWsu9CHKVoaIm4/YySBnG17p0h0lc1OmTPFgMwJIuhN0t9CTHQAOXeakAUk7Ic8VyZxeQORkm5JJJWBlKzHZCohY35GqQamP8StuVsYKXkrmBEHZD2W9sxcQbf5CDIYGHAFc8Fdgv8OAf16d/wGRntTTjz5EMYOsqG8ZIgerrkwuV9bX+HlCvtQQSdvfZ5QYENWuAYadaPFcjYSHx5OAyKqGyIP3lA0VuOuWlXSIjUvlfcVjJJcDok5p4ltUlfqYF5I5tm+Kq8VaQdcyRFbHhkVGll+8y/UaopQ+RF5kiBrEWz3JnJCQFkd1zns/UZkqMMlc/qrHHAdEV1xxhe79sViWO63nOuzE8SsgYnK58n5qa09HGSKDjuhWjj9eIwdqvMtcGhJFfh/IGaIsSebkgGhw6mO8Xt9K3qPNCLmZIUp0KZNL5iCTjYCEBfKjzhODISD3tdR20Mts+tGHSOusmA3JnF5PM4YT220WEJX3VQKbFJc5KbOYIpnLwQwRIAZE6z4WAyIrEpoVax6nDZ3tYFVD5IXLnNZQATAvLk8mlIWtiv65HxB1GFhuA95M+tmY3WMYUPO9eyv4iQxriKz658nHuqYxazYXj7Q9vzy13TaRzAFiYJtzARGnNmCL7W4F3D7gOCCqqanBH//4R6xduxaJhDgICoKA1atXo6Ymj/X9buN3DZGewxygnFBdmUjmLBx/vEa23eYmBWybEp3iBdJokObhvxs9yZyXKerGbeKtXoYoFBJXxZJd/tYQ8ZOPUmn1JxsZIhYQlfVS7st1LbUdzDJE2Vz1ZOetVjLnd4bIzkSHSU0r+itade0igKVkLodqiACg9yjx1k5zVr1jiOFpDZGRqYJUhuyqZE5H6m2mSuDvq9gPqFmS2wFRp5lkzoOawpgmIHKrxiNpUV/mqIbIxHbbzwxRSh8iLxuz6gREkaj43omYdO3r6f77ZgIvmWMLbHksmXNsqvCzn/0Mu3fvRklJCUpKSnDJJZdgzZo1uPbaa73YvvxF7kPkUz+Eph3ibfUQ9f1RB9tl1IfIT9ttQUhNYwNqtza7k2Y24Y8UKZMn9tmSce8K3ZNJoFH6fvQCIsD+hVE7iXS1oZ+0H8MFQEm1+Hs2M0SlfEDEFhiyFBB1tgBz7gF2LXfvNfX6Z3khkbFCaySSTzVE7GJb3s9YbmjZmDUHM0SAveasRs6fgDefz9JljgVhaQZETTuBle8Am+aLn7+9PrUHEaAcq8l4agDMrkOhsLJwk8umCkxSxLaVx4uAiF3negwVb92asFpJ5pzYbutmiHKghshQMpelDBHAlQPk4GIgrzZgNUQdDdltI+EijjNE3377LdavX481a9bg97//Pa699loMGzYM9957L/7whz94sY35STTLEzgtcuGm5uRyssIhT+BySDKn6uXCDcTRYsgFtV3t+nIELdriciA7KerWPeLFhBWG6xEpALpgI0OkedzNgaiLyyRkc1DWmzBkWzK38h1g/p9FGdPEV915zbhGfgH4s+opf6+sbi6bNUQZ2m63cBmihi3S87QZIiOXuRytIeotWW837xQnRdoxm8coaw94Y4FtNenNVDL3xmRg+0L9x1QZIm5c7mpVn0Py8VyuZF1yOUMkB/U6tuBejAd8hghwT9Ik1/hYSOaMMrL8Z9TNEGlriFgmOSYuKmajH11KHyIPXUGNAqLCMjHIyMVeRHyGqKSHOB4ICaBtL1A5wN9tSwPHR9SAAQPwySefYNy4cVixYgXa29sxZswYLFu2zIvty1/kFW2fMkRshUzbCVvOXNmQ8uk1QAX8lczxAxE/uQqFnBsr6DVHjUSVgderFXNWP1QxQNFHa7Fy6GFoLySuZoi41R85IPG4tgowyBBluUEdy7AyiZYbJPQyRD72IWJjgSyZ8+i7TSa5iUWGttu8O6NRhiifXOYAMfta3l/8fe868+dqaxp4/OxDlK5kroFJhwerTQYGHaMOgiKFSvClve6wMamwLDvyz0yR6+BMHEbdrCns0gRELbWi0iJTrCRzYSvJHPcZ9c59JtfUOrwZ/b8XaLP6+ZghatgKPHkC8N9X0t82PQRBPYcKh/O+F5HjDNGMGTMwadIknHXWWZgwYQIOPfRQAMAJJ5zg+sblNdmW+GgxsvZ0ow8Ru1D58dlUjQl13O+6Wu3XbRk1Ry0sEwc8rySBZvVDDNuSOW2GyINi3ILS7NXwJLoUt50ynQxRtmry2CouC87cIK5ZbeR/96UPkfSdypNIj2RG/OTFTcmc0VjG7NqNAqJc6kPE6DNKzH7VrgEGHW3y3Cy7zFnWEGX4nmxf/PxfYrPQrnYxQ6zNnIdC4rjc2ZQ6BrFxOm8CIpMMkTzuuzSO806hLCCKt4tBpB0VhRm2+xAZHBv8Z9RTNhjZbgPicaPX1NxttCYm2a4hAtJvOs/Y+Bmwezmw5HXgyJ+nvXkpqEyppG0sk1we3bJ2zzKOM0QXXXQRdu7ciR49euCZZ57B7bffjt/+9rd48803vdi+/MXvGiKjDFE6fYi0k5h0LK6d0rJHf8Va1ZhQc5F2GoRq7YcZXtdImVluM6wuJowUUwUPVhYLS7MnWWuvl34JiSl4RjaOOR4mK2nb595ryqv73IU9H/sQ7VgM/GUMsPyf9v6fH2sytt3mJHNGiwZWjVlzLUMEcHVEq82fayqZ86GGKCRNIdKVzGn3RUEJUD1YP3NuNC7nW0DUahIQOXFctEO8A7LEvKyPMo66sYJv2YfIYrGHD4J0M0Sa149ElXM6W+Oldg7kS4Yow/kIWxx381oG6JtSsT5qeeo05zhDBAA9eogTlVgshiuuuAKCICCcDT1nPiHXEPnkMmeYIWJ24E5c5owkcx5NTtvrgb8eJmrrp36u2SaTxoRWzfu06EnmAO+tt+0ERFZyA0Y2TBUKyrInWWP1QyU91Cv8Tr/bTGErXJ2N4oXZaBXUCXoF8V4UUVuRaR+itR+JWc41HwJjfmL9/3JD2rD+xNruJFAQgGbOdjtqIDc0lMzlaA0RoNQRWUrmdLKMDPb5XLXdtuo1wyRzSeevLQjm7oNajMaAGJfxzIeAiM9yapFloC4tbPELWAWlYlDUsEUc37QOtE6xatrryGVOL0PEMlDcmBEtFq9B2VpoTpHMeZQhiseU49rtDBE7F9xUOwDKecebUrH2HHkqmXMcxTQ3N+Oaa65Bv379UFpaimXLlmHQoEFYvHixF9uXv/htu23U/C2dDFG2JXMN28TX3rNKZ5sMnO8A51pbM8kc4HOGKF3JnEemCtmqG9OrH2LbAGRRMrdb+V3OWmWInmWy0aTeSwz7ENlcAGATOjt1iACXBSjWt7G3a7vd0aj8TzmXIdIe80Yuc7lQQ5RMKEEGHwSwbAFbJTZCz6mQ4Ylkjq3SeyCZS3RBzl7YMa8xylKrMkTMVCFHXeZibcq2mZkquLWwpXVSZe/pxgq+UY0xw8r1kP+MdjJEgPsZNCtSTBU8yhDxx2tK3XeG6gj22u373KkdY+jNn1hAlKfW244DoiuvvBLbt2/Hyy+/jLKyMlRVVWH69Om4/vrrvdi+/IUdJMkud1fs7OJqDZFWMudxwMAm4olOncajBs53QBoZIs3EkOF5QMQVEhsRsTnRyLapgtcZmjYpQ8TXD7Ft4LfJa/gVLrekBnrHbk70IXKYIWL7xq5hjJnlNmAc2GhhQWpxldgE0GjyaCiZy4E+RPz3rAqMbU60zBqzemK7bRBcMjJxmbOSUmoxylLrSuayYP6SDiwQiRanTnwB5Tt0a2FLe64za2Q3JqxumirYqSECsm+9rR2z2fsn4+7O69hCSGFFqlw00wVoFhAl4+4uFOgpbOQM0V733ieLOA6IPvnkEzzzzDM4++yzEQ6HEQqF8POf/xwrVqzwYvvyFz5qtruS6iZuuMzFDS6+Zk3y3ICXqmlla2YaeqdZBD3bbSA3JHO2M0QGAaMb6JkqeO0yZ5QhyqZkrqtDfeFwS2qgFxjkRB8iaYzoarU3sWWBid1JCfs/vawGYH+SwzvMAcb1V/IkXnN5y4U+RPxnjOhkCq3OX6O6TsCbgM+2ZC6dgMjEIEcPwwwRc5krz33JHJPilvXVz5a67TqpNVApd1HSJB8bRk6pFo55casMkbQP+GA8220K5CyYRjIHuLv4yIyE9Cz3M10M7PDgWgboZ4jczED6gOOA6KCDDsJLL70EAAiFQgiFQvj6668xevRo1zcur+FXvLItmxME5YKgdZJxw2XO68kpf8HTXtjMNPROTRXkiWEWJXOxNmVQ8kIy56qpAhcwZstUodVCMufG+ye6zFewtIN5u0sZIj3b7VzqQwTYC3jZ6rLdcc0qQ2RXNqitvTB6nqFkLgdqiNi+CEdTayMA66DQVDLnweez3Zg1jSCTD5T1ggMtRv3v5AxRaR4ERFwNnB6y66RL44GXGSIrB0LLxqx8hsiG7TbgzIDFDbQ29/x55+Y2GBkqAJkvQPPnQptL8m9ALalnyMdXftYQOTZV+H//7//h3HPPxRNPPIHm5mZcdtll2LJlC/797397sX35SygkGivE27MfEHW1KxcoI1MFqwtvMqFMLLQTGTaBineI/2dUVJkufEDjJEPk1BpaXuHIomSO9bcprDBvwChbBFu5zGVDMleWvQyNYYaI+24Fwd4EyohZk4ANc4Hpi5Xu7Tzawdy1DJFOMO9F3xErtH2IokWivCXZJV48zY5LQFlddpohMpJFyZkei9fjHeYAY7mhpWQuBzJE2n3hNEOkl7Hxo4ZIdplLw1SBry2zg1EdIx/gs4Ao3u6eGYqbmPUgAuzLR+2izQa7uYJvJZmzNFXgM0QmkjlVDVGWF5C0kjnmdCckshcQZdrywgu1A6A/f+pufYjGjRuH9evXY/bs2dixYwcGDRqE8847D1VVFhfR7khBsT8BETsBQmH16i9g32WOH6CMJHOAOGnWBl2ZwgdB2pU+sxVSNsGzm0XQTgwZ8oXXA3kY34PIbFKftmTOY1OFrjZvu4Qb1hCx70jIrAdFvFMMhpJdQM0S/YBIO1lw6yKiZ7vtdhG1HbQymlBIrCNqr7euvehsUY4L1zJEXAd6s2BXK5kz6uFk1ZjVzz5ERvvCaQ2RrmTOhxqijCRzLDi0YagAmLjMcY1ZCzlFRGczUNrT+XZ5iVkPIsB9yRxfXwVwRe9uSuYsbLf1JJyCoHGZMzFV8LOGSM/EyQunOxYQlVSnPpapOkKVIXIzINKZP8kBd6238wSPsBUQvfzyy7r39+/fH/F4HO+88w4AYPLkye5tWRAoKBUnGdmuIWKa0aKK1MmF0wsvkJqNiRYDCAGQOhW7HhDxkjlthshkEE5bMlemvl+uIUozQ7TtW2DPSuDIK1L3f6OUITKTywFcvYPF5E0bAHliu12qXgWKt6u7yLuJocsc936xtvQDol3LlX3atFP/f7RyEk9NFbhJfaaZL7vo1c4VVUgBkYXUiA8W3coQ8RmzeKe4kKSHVjJnlCEylMzlUA1RuhkiM1MZLwI+yxqiTFzm0s0QGUnmysUV/IJScWzPxYDIrAcR4H6jZi8zRHab9uodjylSbzPbbb0MUbYCIgNn0K7WLGaIDKSiduHHdLfk34C+qUKptJApJMS6qFw7/yywFRBNmTIFoVAIkUgERx55JEpLlR0gSDZ+oVCIAiItcjbGpwyR1nIbcCDN4AYsbUo8FBIvPrFmb2RlfECjdUXRG6AYTt1YvLLd/vd0scFi30OAwceoH7NjqABY668Z8oqxJM90szifd+HjB71YWxYCIk2GKBwRJ8CJTmm7eqU81RY7uPYATdv1/8ergEhvdZ+f2CZi9lfLM0GvITFbWY9ZBET8yrLtDJFFJoCfECfMAiKNZM6o3oLJt0Ka1clcqiHKNEOkGxB58PnYZEorK2Zk5DJnkTnUYlRcrs2CFFUoAVGuYZkhctlW2rCGyMUMkWENkYkcWHvOmmWIwnoZoixk1JNJLihLwxHSCaY1RBm2vMimqUK0ECiuFoOhlj3BDIgee+wxrFy5EsuXL8fq1atRXV2NsWPH4rDDDsPYsWMxduxYDB2qIz3p7siRfbYzROzk0snc2HWZkx1eCvTTnoWl3gVEZi5zRs53gPPeT16ZKrBBZ9s3GQREDiVzReXid+qmZI6/mIbDysprrAVAH/feh0c2VdAZSAtLgfbOzM6nnf9VfjfKELHV07I+YurfrVU1PbmnyrUoSwGRnt283WL0dDJEciBoUUMEmB+/fFNW/nna51hJ5nIyQ8TsfLvM6zLNJHNeuOixmhcWhGpxRTJnN0NkJJnT6avVsjtHAyKrGqIsuczFmsVxNN1MO6CfweExu4al9M/TCXB0bbezmCHigzbdbch2hiiNgCiZUGeW3Frc47dHu1hS3lcMiFr3ADjIvffLArYCouuuu071d01NDd5991288cYb+MMf/gAAqKysRH29iw4WQaDAg5UEOxhZbgPuSDMAb4vs7bjM6dYQOc0QGZzQmdpus/qsHf9NfcxODyKA6+FgM0NUWC5O3t2UzGn3DwuIvDJWEAQlmNTWELH3b6/PLAhXZYgsJHN9DhL3qWs1RCamCoA4sc9CPKQvmWO9iCyOeb5hbVe7PZmfVYYoFFKyf2ZjpTyZ1JgqaI/5XG7MapQVUR0HnamtAOTHzDJEHthuW2U0MslKOc4Q2ZDMAf46zW1fDOxepi+XBqwDIrclcyk9xyqVc621Fqgekv5rWxluREwkc3b65+lJ8rKZIeK/g3R6hjnBVoYojeueVmHjtakCIC4k7l2bl8YKtgKi999/HytWrJB/Nm/ejOHDh2Ps2LF4+OGH5WwRocFpxsItjJqyAqK0CrDeJqsu1F46sakkc9oaIrPGrA5NFQz7EGXw2QRB2X5+8s3wSjLHJrSemCowe+ZSoA3eWW/HWpTvV1tDBGQehHc0igM1gzn+aWEDed+Dgc3zXXSZ0+nHE44orkXZMFZIxJVjJp0MkUpqI9jLatmZ+EalSZrRRLCrQ+nVUaExVTDKEGld5uzW5XmJVYaI/Y9RQGQmGfYi4LOawLvRmNWtDBEvmQPcbUJpl3euE+XS/Q8FBh6lfkwQnEnm3Kgp1C5qhULihLVpu3guZxIQ2c0Q6Z1vdvrn+Z0h4scVlalCHmWItOO5mxkiI1MqN407soytgOj8889HKBRCOBzG0UcfjXPOOQeRSATJZBJLlizBkiVL8Morr+CFF17wenvzC7vBh9vYyRAJCXFyZNRUzUyaAXicIeIlc9oMkTRI6vUhMrpgGmGYIcogIEp0KSvUDVvEfjcs2yEIzgMiu6YKrAbEK1MFwLhTvFuwwCNaol+j5LTxrpad34m3BWXiZ2iq0XfCkTNEo6Ttcksyp5MhAqQi3bbsrHry351eQGRVQ6Qtxu5qtxEQ2Zj4Wllvs4l5pEjUqLPfAZ0aIpYh0krmcriGiLfzNcsOZLuGSGtkYfSemTRmtZshMqqlSAmIpOtetjNEggA0bBV/r12TGhB1NivHd5lFQARBDDgytQ3X7htAlM01bc/cWMEqQySrHPQkc1ozIJP/8auGiF985QNTL4IyW32I0phrdWgWBbw2VQDUTnN5hq2A6A9/+ANC2XA/Chp+ZYiMmrICqSuRkfLU/wHMMzGAt406zSRzZlI+p0GaoalCBpI5bW3Wzu+AA84Uf2/dK+3XEFA5wPx1HNcQVaj/dgNtrYnXzVmNmrIyjCQzdmEZu5GnA6veFYPN1lol4yBvBwuIDhZvOxrMFw/soteYFRC/66627NhBs+8uFFZPRAvtZog0kyg7kwJbGSKLiQ4/MWfXIqMMkSCZKuRTDRG7z8rO11Qy53INUaxNWVwzymjIGaIMGrPazRAZLcjIttvl6ttsB0QxzpJ+38bUx9kxXFhhnAHU1hRmGhDpTVrdas5quw+RzrGhPc9NG7Ny424kmxkig7mG3UbSTrDVhygdyZw2Q+SxqQKgZIjccDLMMrau8HfddZfHmxFQ7BoYuI2pZE4TEBUZBUQWkrlMm4WZYSqZM5kQOA1ALfsQpTEAad97x2IlIGL1QxX7WV/ockEyp9WfO3Xxc4pcP2QUEDm0VdfCaroGHwtsXwg014iyOT4gincqF6feByr3dzTo1zU5wdBhLIu9iHireX6RS5YZWSwCaFf97Jxrdia+cnBjFBAxhznuuzLMEBlI5nIqINIJDu3Y+dqRzLkVWLMJTbRYX23Av2c2aoiMFmS6NOOUXzVEfICxb5PO4xpTED1SaskydPOUM0RcQFTu0oQ1qROw8JiaKtjIEPndmFWvBxHgTQ1Re4N463aGiC1oMIOgtn3utXfQnneMPJbM5VfXpHzDtwyRiWQuHLbXGd4s8AAyn5yaYdaY1TQgchjIGPYhcjMg4owV7MrlgPRMFQCPTRU8bFgLKE1ZjTJE8oUhwwzRwKOUDJ3WWIFNasIFYgDE5FlurKwZyVDd7k5vhtFFrMjmqnpKhsjG8eYkQ2R0/GqbsvKvl9KY1Ugyx2VQpHYRWUfeFwYZIsBiXDZZqHI74OPrXYwmUExuyrJyTnCjMWs8xo2B2hqiLAdE/GJBvVlAZCA/BKTvUNrXbmQg9K5xbllv69li8/Cyb+35pnWedNqYVW+c2PK1qMJwC6PFh2xL5thxn+xyvtjBzoEew5TXcOu8MDNVAPJSMkcBkZf4VUNkZrsN2NPhmkkzAOf1Ok7gV0K0k2+z2ibHttsWkrl4h366385rMnYsVi4GTgKidCVzbk2qeXMI3lQB8E4yZ9SDiCFnJdM4n5p2ihmhUATYb6xxQNSqmQQy+283AiI9222A+66zsOpplBWVa4gcZojsZL/NggCGVVCoV8ti9BxDlzkuQPKrjsgqQwRYjMsGxxCgrNS79dnsTOAzksylW0PELYioauJ8Doh4B0Y9yRw7d1iGRo9QyF1JljbLD7jXnNWyDxEXyGiPD+11K9ml9A9LeX0bGaLti4AXxwP/mmpv2+1gNAdyu44p0aWMo3qL2Kqm5A4XA9lcsLyfMh91SzZnNH9ys/lvlqGAyEt8zxDprDYA9hrGWmaIPJwcmzVmNetD5KToPplUBiEjUwXAuW6XvXdZX/FC0bZXKbR1FBDZzBDJpgouZ4j4fcjbbgPemSq02swQpROEs0xd30PE77dyoPi3tjkrWzVlq1xsW9wwVtCz3Qa80aQboe1LwrBTdxFrVQImNknusrFK6qiGyMhUQdOUlX89p5I5/n+yTcYZIjsucy5J5mxnNJAdlzm9DBGbIEYKlfPKL5c5PnvaXi/+qB63sT8Brk7GjQyRzvnulqTJymWOD2RSXOU01y29/0novL7RObJnlXhbu8Z8m51gNAdyO0PEBzl6EslIoTKWOb32yfXklcq1zC1jBb1+dgCXIXIxW5clKCDyErkPUQ7VEAH2VjjsBkSeZIi4AcKwhkgvQ8RN2K0kMfx3ol3hiBQqF3qnKzLsdUuqgX6jxd9ZM1C7PYgA+y5zKaYKLk2G+O9VNlXIsGO2FZY1RBlI5mS53JHirZ0MEQCUZDFDlBXJnIHVvB1nLjbhixYrFz1bGSIHNURGQaG2KStg0pjVwlQB8DEgyjBD5Jdkzgg/XOYSMWWyrG3KCvjnMqfNnmrriOzUEAHcYpgLi1teZoj0+gTx8HMH7XVJe90CdBY2zGy3Nf/LJLXNu9yTwxpK5lzOELFjOBzVn2+FQulfe+XF8QqgtIf4u1uuqVamCl1t1jWpOQYFRF4iT9BzqIYIsLfCYWZvDXgnmRME9YQ3RTLHBikTlzkhab3arpcBYagGoDQzRAUliu0qm4x7IpmT9odsquDyIB0tVuoEvAyCAU4yZxEQpZUh0gZELENkUEPEdPZurqrFDY5dXyRzadQQsQlfWV8uy2wnQ8T6LxmMJYC1e5S2KStgnCESDGqIVBIen3oRmUne7GSIzJ7vekDktWQuzQwRoKxOa5uyArkhmQNS64isLMwZbmaMPXWZszBe4u9PCYik45j/3rQLG3o1SkbnSHONsk1ut0nw2lTByOiGJ111BjsHiivdVTsAxrbbReXKfXkmm6OAyEvsSNO8wCpDZCdzZWW77ZVkLhFTrzammCoYOL8A6pUKq0CGncz8hJ8nXettvtBwgDT53iH1v3FkqmDTMYo9zkvm3Fgh0xvsvGzGC1jXEKXbhyiZVHoQsSBVDog0zVm1On83a4iMbLez6ZxkJHOwU0PEZwycOGjaqSGy2gdssqnrMhdTH/NGkrlQLtUQ6Unm7GSIDGSXQGbyNT2cZIi09R92cJohihaJdvGAct3R67PjW0AkjR1sG40yREY9iBhuZozlDBHfh0h6/46GzN4jaZEhCoWMZZx8TzajBSE9FzurDBGgyGszxWgOxDfPdQO9Y1hLuvW7HVyGyE21A2BsqgAA1y0Abt0O9BjuzntlCQqIvMT3GqI8lMxpJ9vxDnVQYFpUXKBsr9V2GRWXM9JNUbPvOlqsTL53fid+LrZa4ihDZFVDxDJEvPTAhdVvraEC/7tXGSKrGqJ0JXN168VzIlqi9BbiJXP8ZC4lQ8QuIm5kiCxc5rLZh0grmbNTQ8TLCdPJEJnWEJkEA8mE/uo6HxTwq+mGLnNhZbLqew1Ruhkilm3Lgu22rRoiNyRzNjNEoVCqk2guBUTs/Og3RrxNCYjYYovNgMiNjLHeAkhxtXKstGVQ5yFL5kxaSBgpHXjpe8Tg3E8nQwSog6NMMFLJeCWZM+pNBaTfi0ivhshrUwUA6DFUPA/zrH8pBUReIq+iZqGJGCPeqa/P5XFk72okmfNocswGB341l7+wyQOpwSDMbCuZr78RRuleRtqSOe51+4wSB7KuVmDDp8r9JT2sX8e2ZE6TIbLzHDvoSasyqeGxg1xDZOQyl2YQzuRyAw5XVhsr9gMQEvcVf4HQroq7aqqQS32IDGqIutqMMwy84YST+khbGSITO932emXCzfTpgDooYO8hCMYuc/x9vtcQmWWITMZlFoDqTUI8qyHyWjJnM0MEcFliFhCxpqw5EBCxAHLIceItL5lLJrkFBSvJnM2x34p4TPle+Ml2OMwZK2QgaZIlcybtLI3aR/DSd6PPq3etNzpHVBkijXQxXYwWX93O6FvNRYD0M0SqGiIX5d98aYPZducZFBB5iWy77dGKuh4dnLOOVUBktrpr1KWZwS7Ibkvm2L4qrlQGIl7GY2a7DSjBhtbhJ+V9TNK9QPqSuTg3YQlHxEk4AKz6t3hbNcjeqknE5uRG25iVvy8T9PrVeCmZS3SJEg7Ahsucw4wrM7VgGTtAvAizoIeXzXllqiAIJqYK0gU/q32INPIM/vgxmkjyReHy2OYkQ2THdltnksEu6gVl6slRRCdDxPfD0UrmAPezKE6xlSEys902WZVl+0ZIZC6bFQR7JgCuNGa1mSECUqXaesczC+5jzelJ+dJBEJQFg8HHire89XZ7vTKW80G9Hm65zOlZkjPc6BUjZ3BMAiIjt1R+wVUvQ5RMAJCOYd0MkeZ/+SCIzxZlgqWpglsucyyoLzf+n3QXA3m1kOstJKTvx0hlk4dQQOQlBQ4mDW7BToDCilTJCMOWqYJFJsYrwwg2OBSUcSt9OgGRUaDmOCCyksylmyGSBk1WxL/mA/HWjlwOcG6qUFCqTADdWLnSW/3x0lRB/r5Cxhk0p413GVpDBYae05xXpgrJOOQLSIqpQhYzREZS0WiRcswZLQK0cvvGSYbIahEDMA8G2PddpJkwhMPKZIw9j5+Y69UGys1Zc7mGyGBcTsSVSbXe81V9ljLMEnU0KN+bWc0LL5lzGoQ5NVUAOGWCDckc4F0TaS2xFuVcYAFRc41ynWET9pKexkZFDLckc+xcD0dT35MFuRlliDKQzPGLQ3oZIj6AUtUQ6QQjrXvVks1mLjjKBCNHR7czREYyZp505yN8Pbmbagc9F9oAQAGRlxT4kSGyaMoK2KwhspDMedWThh8c9JyvrBrGshV9y4DIIt2brjxMm3liWQkWqNoNiIykBlr4QdtN6ZWuqYJHRhqAsmpV0sM4kE/nfIp3AruWib/zGSIg1VghHlOyVLJkzqVVNf5cS5FguFyka4ZRHyLAuo5IroHo436GyEwm1Kkji2Jog0l+YqQrmctA4uUGmWSIzFoFAO7airOJcnGVEvzqwWfhBIfZGKemCkBqbaeeZC5apIyf2ZLNsf1VUCaO8awHYP1m8VabeTZDHg8yzRBx7mVaWJCbiQuYnumBFiOlA7/gqpsh4q57Vo1ZtRkht0wVDCVzHrnMmZkqpJ0hYjVEFe7Ww7J5TqTQ/PvPMygg8hKvaoiSSePMjJWhAuDMZc7oYuXV5JgfHAqZ85VeDVG2MkRpusyxQXOAJithpwcR4KAxKzdouym90lu1MivsFARgz+r035sZKhjVDwHpGV3sXi4eMyU9geqh6sfkDJEUEDH5SDgqFh4DXIaoIbOsAj/RTzFVYJP6LMi4jPoQAfoZWR7dDJFLjVnNbLf1rJUZ2skjP/Eyk8zlYw0RP+brZoh4W/FMAyKbTURVWSmH50c6GSLtxFDv2AiFsl9HxNcehkJAT8ldi8nm7Dj2MXj3xEwwK9Zn42wmzVn1TA+0WJkqRPkMERfkqDJEFjVEWhMF1zJEBm0SXM8QcaoYIzKuIap012XOav6Up1BA5CXyKqrLsrJZlwMPH6wf6VtZbgMOM0RGkjk2OXZbMsetYOtd1Mz6EAEOAiKLVRm5hijDDFH1ELWNtJuSuWRCWRHntdiu9q/QcZnTG5TXfwI8cSzw8e3pvZ9VDyIgvVWyHVz9kLZ2S9uLSJ7w91HkVrJ8T7A26mCs/wTYu059H7uAhyI6/XFcdJWywqgPEcCdb02pjwFchqifs7EtU5c5tiCiFxBpM0QqyZxZQORS8Nne4KxOJZMMEb/YoleH6EWGyElA5NRpzqpOVQ+tdMjIsjjbAZE2AyQHRJKxgt0AE3CvMatZsb4bzVnZOWRWQyQrHYwkc3wNEe8UyR2//OtHdf6XZYjYWO1ahohzwuNxvYbIToYoDZc5rcEWL//OtMbQjhFEHkIBkZekY7ttZ5V461fihH/3itTH7GSI7PRHsrTdZp/Nbckct6olS+a4FWtLyZzPNUS8qQIgTlz42hXbARGrdTA5HrSraF5I5lSmCiYywpol4m3t6vTej9m/mgVE6TQD3rNKvN1vbOpj2oCId1FjRAoU+YudlbW6DcDffgK8MVl9v+lE2CWJjB3MpKJmvYi62pXApLyP+xki04DIoIYISN13vGxLb6ImS3hcqCFq2Ao8dCDwz1/af04mGSKrjAofnCTcyhBZZDRUvZ0cvqcrGSIL10Sj4N5tWrjFFEDpvyJniBwERHqT/nQwq03JtDlrMqmca0aLpvxj2uORl+TrXbf47BMf/PPBCJvUs4Bov8Olv3e504vPaA7keobIhu12OhkircEWk8wlYpnX1skBEWWIMuadd97BiBEjEI1Gceyxx2LVKnHSMn36dIRCIfln5MiRfmyee7CDJdmlf4HavQL46jHg3RuAF88D/nQAcG8f4PMHjV8zmVQOdD17SdcyRAarIwx2gibj7k7k+MyNbobIYrtKqsVbq4DIdh+idE0VuNfla1ecBkRmATJ/AeGL4l2RzLGJM/c52CpVvD11VZzJFlgNm1NYttNuhsjuqrzcaLV/6mMpkjkDWQu7kNgxVmDNd+vWqy/KZsYC2TRVMJPMmdUQsYlTpEicbNptOi0INmuITGy3ndQQqSRzeqYKLkrm9qwW33fbt/afYxoYW0y0rJwxQ6HMbLB5bEvm+KxUmhmidGy3U2qINMGyb5I5aX/1HCHeMuttbcBkhluZfrN6weoh4m3d+vRe2yiDo8VQMsd993rXraSBQkU+VgTl2sgCIuboGu9I/zqk2kY2ZhsERG6N12bfEyMddYRssFUuLpYUlCrjbKZ1RJQhcocNGzbgyiuvxP33348dO3Zg6NChuOqqqwAAixcvxnvvvYf6+nrU19fju+++y/bmuQs/mdTW63S2AM+eDnx8G7B4JrDlC2lCJoiSGyM6myC7Vek1ION9542w1QDQymWOO3ndNI3gJXN61tdW7ndOJXNu227rZZ74gIhlJayw06yTfyxcYC29qlkC1Cy19/66krnS1McZ7KKU7oXITg0R/13ZcTcDuN5GOoEW7zLH2wxrXbWcGCuw8y8RUx+DZg2F3eo7YgdbkjmdY15bI2FXNpKMKyvJpjVEJsYSZra0Uc3kkU3KQ2FzWZkbARHbrtY99lekTTNEFvtUzj6b2ZezzLJbkjmLDJFKMufUVCGdDJENlzkgByVzNiWIALcY5mGGqP8YACFxMSidOqKkRp1ghJHSgb+O62aIDBzs+GOFHT9sHtRjuJLNd6MXUbZMFYyOYZ50Fmj5pqyAOB661ZyVaojcYdWqVbjvvvtw6aWXol+/frj22muxaNEixONxLF++HCeffDKqq6tRXV2NigqTSX0+wJ+82pXU5hpxUhcpAk6+GfjxM8C5D4mPmdUq8BNOvZNeexLoYUfuYiWZixQoq5FeBESFpeYZIss+RA3m72PZhyjdDBHT+XMDxeBjxEn2kOPtr4Yaaa952IAdLhBrXqImQVQ8JmYhXzzHXtpd72LKf6aUgIhliBqsX1sPJzVEgH0ZKgu0SnUCLRYQxTvEFTOjTvJO7Er585NfsDBabQSc9R0RBFGqla4kxFQyZ5Ih4uurAPtyYD7AsZMhchoQaVeXzZqy8ve7YWDBxoZEzL40y1aGyOA40BtbtLhVI2V3As9n4RxL5tzIEFkERNmy3dZmgFiGqGGreKw5MVVwS5JlJY/tJSlwdi1x/tqqxbg0+hDx9Tl6n9eo6Ss/H2H/zxbjKvYDKiQlgN5isVNYgJYLttuZZIj4xXHZWCHTDJHF/ClPyXpAdP7552PatGny32vWrMHIkSOxdOlSCIKAww8/HCUlJRg/fjy2bt1q+DqdnZ1oampS/eQcoZBx8TGbAFYOAH54O3DYZeLEGTCfVPKP6UrmnNhu2wiIjMwLQiGuJ4SLxgpWkjnXaoisMkQZBkT8yklxFfCbZcAV79p/HVuSOc2+0LMvZXQ2izUgsRZ7dT56F9Nw2NiOnJfMpTNZbzMJXPj3Z+eT3e+lzSTzFC1SJjBNO4wlc07ceXjdNm8Ha5YhcmKqsPA54JFDxaxyOphdfM3qLrQTOrurpGZ24zxmWTLTGiIDyZyewxyQWSNRLfzkxO4qeyYZInlsMQksZVvxDD+f3Qm8Sqbn8D1l2VQGNURdOZIh0gaQ5f3FzyUkgMZtDk0VXMoYWxXr73eYeLvze+evrTIvMckQGS3sycFGof51i/2/9rX1stPs2lO5H1DRT31fJrBrr9eNWeVj2KQxazo1ROzY5+eCTuTfZpBkzn1isRgeeughXHfddVi1ahVGjx6N119/HStXrkRBQQGmTp1q+NwZM2agqqpK/hk82KadcbZhFy+jgIhfEWdWv3YzRKaSuSrj17CzwmGVIQKUSb/ToMEM25I5twIioxqiNCVzRp3kC4qd+fXbMlXQBK1m2mbe/IIZDZgh15poLqZyEMwNzMmk4uwjJNNblbWTIQKc9SJKJrjaJINAi5fNaZuyMpzIDIwyuGY29k5MFXYvF283z7f+Xz3kSbVJHyK9769Vkz2znSGSJg2RQv1GqQwz223TGiKt7TbLEFkFRC5K5gBl/5iRTCjns6mpgkUfItMMkVuSOQcTeL45q11UTWadZIicusxlaaFUu5gSDivGCnvXKWOHkwxRxjVEFpNWFhDVpJEhYsdxKGxxXhuoFuRgo9DAdpv1ONIJtvjzJNGlnHsV+ym1onad5gQBeP9/gXkPpT5mKJnzyFTBLLhIx2WuQydD5FZfPasa7DzF14Do9ttvR3l5Oa655hpMmjQJCxYswLhx4zB8+HA89thj+Pjjjw0zP7feeisaGxvln23btmV5623CDnJtzYPeBJAZAiQ6jScafLCUtqmCjcmMrYAojTSuFZaSOQurVhYQdbWaD1heucy5pa21s0qYkiEymVjz3/Weldbvb1RropchaqtTT8Ds2lPztJrU+vDoBWRGtNdDrrdjFwItstPcdm7Sryl8drKqxk/A+AULs8ymkyJqdi7UrrH+Xz26uPNLi9mqujZYtJ0hslknYiYXs1VDJJ3rrI4lKwERdwzasS9WyQfNbLeNMkQ2aojc+HzJhJJZtRMQpWPkoDKEcaMPkTYgYtnOLGSIBEHfoZLVEW1fCEAQgwerBR/AvJ7OCVa1KZkERHZ6EAHKIqCh7XaRge22iaU3f56wOVC4QMzkyxkimzVE+zYC3z4NfHpfaobT0FRBx+kuE+xI5jLJEPHlE07k32YENEPkW4vZ//znP3jqqaewYMECFBSknlTV1dVIJpOoqalBZWXq5L6oqAhFRQ5WlvzCyI1JLyAqrBAHTSEprjTrTaptZ4jMAiI7GSKLPkSAs8mpXfgTTWu7nUxYF2gXVSr7sL1BGSCN3sewD1GOBERCUvzcehM8o4BIL0PEfw47AZGR/lxvv2g7hXc0AnCQsRUEBxkiBxcGVj9UXG18HPPW24YZIge6a6MMkVkvHieuRexc2LtOXEV1knWMx5RJq97xaWaqoF0Bd1pDZNVrxsxy2lYNkVPJnIs1RIA9+2L+s2WSITIbW9yokWrdK447difw6cgQrYJDI7TW/0ZNe7MpmetsVr4bPgPEMkRbF4i3ZX2MA3UeO4Y6drCbIWrYIi4eyT3XbMDOM7P6IUD5LMk49rXG0NIRx5BepTZMFUzmH/x5wuZAFf3FTFXFfuLfdjNEDVvEWyEhjt38wpmV7Tb7HyfHrx5m4xujII25Vqd0LVJliMhUwQxfMkQbN27EpEmT8OSTT+KQQw4BANx4441444035P9ZuHAhwuFw7krh7CKvaGkmDrKrFnfBCYfFehPAeJWdn3B1NCirhvJ9Tmy3zVzmTGoeGLJkzosMUXlqTYOqHsFgghsOc9JDE9mc1xkiM1mLHfgLjdGFUZt1MDNV4AdSO5I5o1UrvaygNjB36jQXa1EuhlYTMCe9iMzqhxhMMle/RckAeWmqoJshko5lO5I5NsFLdCoXc7vwcgs9yZyZqYJ2BdyOdT/gIENkYrttq4bIpmTOzT5E/P5k47kZbF+FIvqBrN0MkalkzoUaItlt0eYEnkmmnLjMsc8YLrD3HowCblwWBG4y6WMNEcssF5art4NliHYsFm/tyOUA92ydrTIPJdVAj2Hi706zRCwgslqQ4ZQOE5/5Gmf85XPUtXSqzZHMbLf1MlD8eSIbKkhSOZbRtFtD1MApi7TzBaPFHD2nu0ywk20x6wFohFxDxJVPOKmHNUOvT2EAyHpA1N7ejvPPPx8TJkzAhRdeiJaWFrS0tOCwww7Dbbfdhnnz5mHu3LmYPn06pkyZgtLSPN/hhjVEBn1X2GTeyFhBe79WNmcnQ+SGyxzgvWROW9PAp93NAjU7dURWul3+vZ2kxd3OEAHGUionpgr88ddcYz25Nxqk9VL3KRmiBvPX1sIG52iJufUovz12LgzyooNJ7w+WIWITglBEuWgwHJkqGAREsqOWTmDgpA8RP8Fz2gSXHQPhqL5ZChszYnoBEasp0WSIrOzP7TqJmcmEOs0yRJpj3q7LnCuSOT4gcpAhMgoObWeIPLbdduKIBqRnqpCO5TagXhCJdypBWEpj1gwDok3zgE026/SMegyxgIiNpdrMsxFuSebs9LdJVzZnVzInnW+Jrk6s29OCWDyJDbWt6muXme227sIBl03mM0T8rd2AqJELiLTXRCNHW5XTXYZ1XoA92+105lq6NUTSfDNjUwVymXOFjz76CKtWrcKzzz6LiooK+eeUU07BxRdfjAsvvBBTpkzBWWedhUcffTTbm+c+8sRBE3wYSYTkxqIN+q+nXX3XBkRuZYhyQjKnkfCoAiKT7bITEFkFLvL+E+xnPASBm7RkOFDwn08zuXlvaQ1W7mzSMVWwKZkDrCfTRoO0XnFnphmiVptyOcA446qH7Fxn8rosQ8QaFJb1Ti0STttUgc8QscBAJxAxy+xp4WuU7GT6eOS6MCOZqJntNssQaWqItBlqLU4zRI77EGlq7WxL5tyuIbJhqmAVHOqMy41t3DGRLdttJ4YKqvd0sE/TsdwGuMx9m3pMS8kQmRzLVrTtA/72E+DVi/Xlo1q0iwUMZr3NsLs/3ZLM2alNSTcgMmqcqkX6LB2dnfK6Yk1ju1rdoBcAyq9vUXPJW24DnKmCzRoiVYZIEyQYqWRCIXMTGCckk9byff6xeIf9hQe5hogPiKS5kWs1RCSZy4gJEyZAEISUn2HDhmHGjBmor6/H1q1b8de//hVlZRarxfmAle220wyRNlBSyXK6lAl5xjVEBqsjPJ5L5jQXNX5VSa/pIkMOiExOeqsVjoISJdVsd7WJHxzNVnHtEI4oPT64QHDh5n24/rX/4sY3vjfJEFmYKgDWdURG+4efkDB0a4gcYNY8VYsTyZxs1GAimatijXKlq7XeqjjTlXc0WF+MVKYKu5XsIt93Q4tZZi/l9fkMkUNjBXnF2OAiZlRD1NWhfC5mOMFeI9EpXtSNsDOOABa229L26EnmtPuObYuR85WrfYi4/WTHdtsyQ6QOCp+ZtwGH3fMxPl4hjT8umyokkgISSZ3st+OAKA2XObOaOjP4BRm2/6MlqbI7Mwt5K7YvFI/DeIeyUGKG1oGRUTVYHZhnWzJnR4q13+HirVPrbds1RGLA1N6hXH92NXaozZH0XPXMMlD8wkGTRjLHaoZjLfaCYdMMkVkdk03JsBV8ht1OhgiwvwCtpxYiUwVTfHWZ6xYYFR8brV47qSEC1Csh/ADArwposeUyZ6MYWl6t98B2u7BUNJkAxEEjEbdX1wTYzBDZsLqskLIHzTvN309+TW5/ZlpDBOg6zc1fJx43G/e2QtDWEJmZKmi/o90mAVEyobyGoWROJ0PELl6OAyIbmRyGE8mcnd5G7Dtm6MlamGSOmZ2YwT8eb1cuSma223b7EAlCZpI5qxVjo1V1JgeLFCoLNnZ19HYzRGYrrmaSErlflw+SOX5i4sRlzjBDpN4H32wUJy1fbZACe1u22/ZqpARBwKVPf41TH/oUbTHNvvBIMrehtgX3vb8Ke1s6M8gQcZJds+MiE8nctm+U3+0EREZmLJECoJqrgbadIXJQU2iGHSkWyxDt26DuoWZFwllA1NmhjG01jR2aGiKdBSGzGiWVqYImQ1RUoWSS7TjNmWWITFsluJQh4q9jZud1QQkAaRHY7gK0boaIUztk4pBHpgpEWhhp7Y36ozDJnFUNERtc+YCITcYKSs1T2W65zDmRL9lFz2UOEOsa7GwTwNkkZyCZA8RGb4CyCmUFe81IoTP3LyPkpnbKavaCjeLkKBZPorWtTXk/QCW92tcak/9XfAL7X+m7N5NbqaQoWlMFPcmctH96HyDeOrXdlrOlJoGL/P5OMkQ2TBUKitWBmN4kMFqorLKZrawJQuqkgl2UzWy37fYhineqpVB71zqr25DPLYMJEvuMbAGC0cLJ5Vhmlj9v7JizWGaIpMeFpPq9E3Hl9c1st+MxLNnWgMc+kYLEbDRmVdUQ2TFVsJkhSnQCgoDt9eJ4sqVOeh8nGSKLDNimva1YvKUe2/a1479bGtQPppshMtmnbbE4fjlzIZ6ZtxGvfbM1/RoiduwmOpUJn5WFvNOJ37Zvld9tBUQGkjlALZuzG2A6qSk0w84qfllvoHKQ+PuuZbZe9u8Lt2LhRukz25bMKWNEimROkx1OJgX7GSJtDRHAzY0slB2JuNiQm5FiqmCS1bebIepsARa9YByc8bXMZv2cQiHnC9B65RNscS/RmVn/SKohItJCL0MUjykrx9r+KFbNWVnQ0/tA8ZaXc9kxVOC3ycxH346pQjre+Gao9LTlageazhbzFRseqwxRMqlckL3IELmRHQK41W/x4tDRlcD3Wxvkh5tapAFNx1Thf/+xBBOfWaAERWzb9hsr3u5Zafzdy8FGKHXComuqIB2DfUaJt45riBxkiBxJ5qSJvFWgxeqIAGMDBnZMmdURxVqVDEWVtDLMgkXTDJGOZEQPfrU7UiQeww1bzZ/DY+UMVKhZgGDIltvcvglHlMmK2YKIXWkU/zgfYPGyNDNThUQnnvtiE75cv0fZPj28MlXobLJRT2UzQwRAiHdge734fW2pk743O/WJNj/fgo1KYP/frZpx0mmGyIZk7v4PVmNzHfd5Ms0QAcpxqXdcsIAoGXe2ip+IK65wQGaSOUCx3jZ6XA+3GrPaqSECgAGHi7c26og21rbgd/9chqfmSgsPlqYK4uOxTiVw2MVniCKFquvWVxv24rC7P8Zis4DLLEME2DdWaK5RH7Mpkjmzuk+bGaLvXwNm/xaYr9P4FbCXxWM4nW/p9SEqLFP2dybGCiSZI9JCT57GDsQQZxHNkDNEBpNKdn+fg8RbVYbIhqECwF2EBONB12xFm5GON74Z/Ouwk59d2GIt9jNEVgGRSrdrckI7zhC5XGgoO0aJn/u/W+sRSyj1Gs2t0mCqY6qwqqZZfo64bdL/7neYuHre0WB8weAHaW2tlnaVKhFXJibsmPSyhqhAp4Yp09dlK6SA8aTFjrECW5AIRxU7W3Z+mvXjMauf0Xv9wgolG+ekjsiqu3iUm5zwdURGkiBuYaWjK6FkMnhsu8xxj/P7gQVE/EqydpsBIB7Djvo2RMBqiLLsMgdYy+bsZogANDa1oDUmTta27mtDPJHkbLczryHis8eLt2gDIocZIgvJ3Bfr9uLlrxWL+G31belniKLFkKVD7LjUm0wWlCn/50Q2t3u5+jqUiWQO0GSIsiyZs+MyB3DGCt9bvuQi6ViJx5lkzsIyXfosXV1KQLRTJZlTZ4jmrtqD5s441u2SjkmzxqztDYpiRi8gsjJW4OuHAB3JnFlW34YxFSA2/AbEPnd6OAksnDrNyX2IuPlgKMT11cvAepskc0Ra6GWI2IFY0jM1TWrXVIGtxqeTIbKj/7dlu80+mwcBEQskeecrt2qIYjrvowcbZLWmARyfrdmDc/86Hyt2NnJZpwwNFRiaGiJ+VRcAmrWSOek2Ge/E7iZxW9btliaU7Pgr6QH02l/83chYwSwdrjVVaK2VmjhGgF4jxfsc224bWNDr4eSYkzNPTjJERgERk2GarKp1cBcg+diRzk+zwMCuza684leunP9O6ojsTJDY5+TrKFp0MkSAqun03e+uwKkPfYa5qzWTENs1RFFlYs2PSbLltsE2cxmimsYORCFNykMGlza5D5FzU4UnPluPBz5cDYFlVllAxN7LwGlu3e5mvLFoGwSrbFk4Kr9WTV2DfHc8KWBnQ4e9xqw2+iwJgqAKiL7bWi/KlBjy9525y1xjexdu/oeYeThiSDUAYEc9J5lymiEKhZRjQe7/o3NshMPp1RExuVzVEPF273pryZ1ZRq1nGhkityRzdjNEDpzmvpMUClGwGh8ryZz4eDymfJa9LZ0QDDJE26SsaEdHh/Hrs2OG9WGLlqh77TCnOZPrtvh8TUCkzRCZypxtGuGwOYjRIqFRY2E9nPRG5OtNtfXkbhgrWLUtyVMoIPIauecPFxCZSYTMbLfjncrrMMlcOhmiSCHk1TM9mYcgpCWZ29XYgT3NGRQZxrgJGwsUZbegZnvbBFgHRGwyHS021+2yibLB6o4gCLhn9kqsrGnCv/67w/00skYyxyYx5UXiBKQtJSASB+nOjg7EpQnOml3SoBjjtq3vweLvhgGRyYVUu0rFLjrl/ZT9nrapgo0aIrtW78kklyFyEBBpJ/0MOxki9rmLqxS3I3Z+2pHMJbvMHdv4CxzLxjkKiKSxw2yCdNQU8fbj25X3azVYAef6mX2zaR8EAXjqs43q/3HiJqany5cnDAYmMVG2CBDD7qYOhOUMkbs1ROt2N+PBD9fgyc82SH1U4sp3WiVlGHWc5pJJAb98aRH+9x9LsaGG9dsyCA5DikR1974G1UOb6lo5Sa6dDJFxwLdpbyv2NHeiMBJGcUEYTR1xbNzLLZywlWWz/l2q9zSWzN3z7krUNHZgWK9SPHLZ4QDEGpK4nc9iBBuD+IaoesgBkQOzALYQMPZSACFROsoCHj0EgZOU6gVE0uJTtDhVDWKEExt+I1TGODYzRHvXWk62v5MUB/LCg5VkTro2dXUp2S5BEBCSr+VFKle9bfvE46KTSezMaojqN4u3Ff3VSgY29lqZKjRKcmM2rqVjqmAVtLJ5nNExyJtIWeEkQxRrVXp0aeeDcoYoE8kcZYiIdNAzHjCbqJlliOSJZkiRzLTWKhd3uxki7sKrmyFKxiHbEOvJVBicZK49lsA5f52H0//8OTbW2ujdoIfe4MA7X2n77hhhGRDZPJktMkRfrN+LjbXiNm/a28oVPbs0SHCmCnz90Pgx4gpYWztn4gDI+6WDszhdX9siWuuy7EBhGdD3EPF3I2MFs9Uf7SoVX9QqH7tpSuYcucxZXBQ6GpQJmtXrsuasgHGGqMTGRYRfkJBXKVmGyKxAlzuezTIXmQZEMRsB+wk3iHK/5hrg8wfF+4xWwKXsaqKzDdv2ia/97eZ9WL6Dd9pj56yNia+edJDVMllkiGKd7UgK8Ewy9/q3ymryqpomdWEzk0fqZIi+WL8XW6V9U9coHR9mwaH0WG2DegK1hQ+IzMYX6fN9snyH/J1oYZnmI4ZU47BB1QA42Rz7riNF6lV3M1iGTBPMf7xiF/753+0Ih4A/X3oYhvQsRVE0jKQANDczy2yHGSJAuT6wbTU6njPJEA0/CaiWskRmsrnOZuUaqjd29BklnlNn/595qwgeNxqzmhnjaKnoL45XQhLYvcLw31o641izW9yXBXJAZM9lLsl9lkLE1Y/LnzcmZ4g6WUbJLEMkB0T7qR+XexFZ1BCx+ksWEPKL0Mkk53TnRobIICCy04OI4aSGiB3zoUjq+VFiQ+1gBQVERFpwshIZeQLYM/X/zWy35RXoSnFFPhQWBzF2IbabIQLMT2hVA1R7krn1e1pQ39aF5o44rv3bf1OtXO2gl2Fhq3+xFvM6DB45IGqweB+LQYgNtC17dFfrXvpqs/y7GBCxzJNbNUTKBJHVD/WrLMJxI8QJfrssK1BniGKdyrEWiydTJ1NyQGSRIbIVEHFFrVZNhY2w4wYnv7/NVTJ2jhVVWk+6qriAyEgm5DhDpAmI7GSIANX5uGZXM058YC7+9JEU9Mj9ePiAaK15VonHjtV8QQlwjhQILXhCDJqNisalDNG+xkZ0JRRZ0YtfbuY+j4MMkbQfFqzdidvfXobOeEI5zvR6EHGvy455OSBysTFrR1cCb323Xf579a4mZVISiigGGjo1RK9/q5hetDATFLPgUNoHdZqAaNPeVnvyQ+nzzV1Vg/95U18CxTLNx43ohSOHimOl7DTHy+XsTuB19unG2hb8/l+ia9k1J++Po4b2RCgUwsAe4tjY0NRs/VmMKLAhmQOcB0RNNWLWIBQGBh6lLDrWrTN+jlzHVK4feIRCwJn3AOOusrcNgDuSOTNjHD1YUGDSj2jJtgZZPRhhAZGVm6q0qJeUMkSF0TAK+IAoqmSIEl0daO4QH+uKSXMQsxoiptzgHeb4v60yREwyx0wl+MUuqzmQ3RoinQxRQ1sM326S3ouN6VZzEf5/7LjMyYvjFannsZNG43oIgrNALo+ggMhrdGuITGomzGy3+QlXOKJIGpgsx26GiN8urR04YD8g4lYsNnBZoTW7m3Hbv5YrWnu76Olp+YuaU8lcZ5O+7MCu+UFZH2lAFlIKNLfWtWHOamUCtHVfGxJWRetOkU0V4vKq7nEjemG/KnEw7mSZIDbZlG7jnepBeu3uFnXWRw6IVutPps205ymSOT5DJAXzsWa1dbIZibhyrLvZh8iJcx3LEIUi+osUgD2ZAV/EqrV+NdOj8/dJx3hnPIEbZn2H7fXtmPXtNvFc4i9yPYeLk42uVqVw1wo7kjkAOPBsYNR54gT3/ZuNi8alwL+2XvzcpYViEPLukp2obZYmc3LtlJ0MkXj8vvrlWvxtwVZ8unqPjRoiSZIjHfMRS8mcdL8DOdJHK3ahoU35/1U1zWrjEXkcVmeIaps78Z+VyrjR2mojKyI9tk/KJo0eII7lW+rauAy0tctcFHF8s2kfttapFw4EQcA3m8SJ0LEjeuLIIVJAxMxXzCykDd9TkcwJgoDXvtmK8x79AntbYhjVrwK/PfMA+V8H9RC3vcXOvjBCmyFyKyDaLmWH+o4Wn8tqIs0yRGZyuXRxQzJnZoyjh406ou84N8KCkDPJnCB9loP3q0QhutSPS/+T4IwXouz1zbIzTMWizRDZdZljpgrss3e1KuMVH4ya9iFykCGS5kO3/HMZLn36a8xbW2u/zov/HycZIr25YKamCokuRX1BGSLCEbzFNcOsESWTHXW1pbrMsNUG9j9s0tWcRkAkndD/WboFD/9nLWJxbmIsD8Qh85Q4NzlmAdGhA6sQCYfwr+924G/fOLAEBiwkcy32AyJe6qEn37Kb7g2HlfS7xmnulQWbIQjASQf0RlE0jHhSQAOTxLhmqsAkczHVqm5/KSCKscCH/R8rYO1SD9JrdzerjRJ6Dpdsm9uBhs2p72tWfK81VeAzRPx+t6vbl9P2ISWQNcNu76s2B1mnniOA464DzrjLeCLtxFShuDp1ldIsQxQOK+eZdIF9+OO1WC3Vf9W1xrCjoV19kYsUKBM2u05zTgphx88QA57N85UVcqMMUYP4uY/fvzcOG1yNWCIp9poBHNYQif/T2Cx+zmU7GpUVVKM6EXl1Wdxv1gERW2SwX0PEsjwnjBTH69W8ZK6wTNkvGsncPxZvRzwpoCAiTkjb27naRSOkxxqaxM994gHi8bt5bytnqmCdIYpK++Efi9WF45vr2rC7SawfOnJIDxwpGR2s29OCxrYu5w5zgJyNa2rrwNUvL8Lv/7UM7V0J/GBEL7z0i2NQFFW+i0FShqi11Ua2zIgCbUBkVUNkMyBicrnBx4i3ckC0wfg5Zg5z6aLXqNQpTvvE2AqIGgAAvcoKlRoim6YKYUkKfOjASkVuF4qI56l0Dgvc5y0we33tMaPNELFjt7PR+DohCECjtJDUb7Qi+2QLXvzcy0GGKGURmC32JbuAeAe6EknMWyeOE99sqnNmu+2kDxGvJtKSqakC//5kqkA4Qrbd5qJ6s0aUqsl8g/ox9jf7H9leUloJcSSZE0/ol+atwaNz1uHKmd+iuUMKhHhpmtnqkk5AdOHhA/C78aID1r3vrsT32xoMnqyDnlSLBXcxvobIYnIVjnDSQ506IifpXma9zdURtccS+PtCcaIx5fhhGNZLfJ36Rq4xrhtIA3Es1iHXDx03ohf6V4rfnaowlbtNSpPDIT3F7RADIm4lKhxRXMp268jmzDIJ2kGZzxBFCpQgyq7TnOy42MPawpXfJivJnF2HOUA8xsfPAE74tfH/2JLMsfOPk8zFpGyCldyT60W0YGMdnpkvmhNUFIsT3GXbG1Ndg5w6zTk57nsMBU6+SX2ftsheGkPqJfnTsF6l+MUJwwAAf/tmi7jIYtcZEkjpv7J8R5N1QMScFeWAiJts6eFQMrextgULNu5DOAT84fzRAETb4OamBvEfCkqV/cJJ5pJJAbMWioHUT48Ra1E6O1hAZJ0hamgRP/cJ+4vH77b6NggObLfZfvjH4u1iDaEEW1g5fEg1igsi6FVehGG9xHPqu231znsQce953+zl+GTVHhRGwrjt3IPx6lXHyos3jMFShqitjQVE6WSI2KKMRX2ZU1MFZqgw+FjxlgVEe21I5ozMWNKBjRFCwjJw39caw7LtjarvGIB1zzEtTDZWu0rXaEkQBHwnXctPP7gvZ6pgz3Y7GkqgMBrGyD7lKAxJ84yo+rrFZ2WiZjVK2hpibYaouEqZdxlliVprpWAmJLZdkPs/SvMFdm0NF+jPgXQyRDsb2vGDGXNx3atSH6t4TN1HraMRy3Y0ok2y01+5s8l88VELGwMbd5j/H2DsMAdkLplj84Nw1DogzjMoIPIaWTLHZ4hMisjDEaBImsxrsxvagIhdtNLKEIkXqqggntBfrq/DpU8vEO2a7WZiOMevDXvEE3v/vuW4+qQRGD+6P2KJJK5/9b/Y1xozeRHgrf9uxx9nr0SyU2e1RGW7zbbLxkloZqzgpCBQx1jh7e93oKkjjiE9S3HqqL4Y3lvc3sYmliFyy1RBvBhs2dMk1w8N61WKsqIoKoqjihab7Y8okyeI3+mpo8SL9NrdnMSHDbxmxgry/+p8Dm3aXg6IpP1UbHDsGrFvk3jLGxuYIfchasXD/1mL2/61TLYYVyFniGxI5uxgYKqwfk+LogfnV+WKKpRtbd5loymn+N21tLXhpjeWQBCAy44ejPPHivt16Q69gMihsYJTSefxv1ZcssIFqRk8aQxpahaP+6G9y3DOmP3Qt6IItc2deH9ZjbN+M9LEqEiS1Czf0QiBSeYsaojY6nIkJGZGkka22w4DIrbwccqBfTCqf4Wc4dixRzq+DCRzX2+sw5a6NpQXRTHtFHEfJmI29oX0GAt+jh7WA4XRsFijZUfqG1FniHY2duCrDXvlh/lMM0ORzTWklSFKSPu6rSOGA/uV4+3rT8DVJ49AOJw6kWT7Tw4O7QTKWrQBkNGkn3cotaKrQ6mfGTxOvGUBUf0mYwmwkQNjJvATfulaHE8ksaqmCX9fuBV3/XsFLn92AY7+4yc48t7/4ILHvsBVLy1ERxcXPGnHeysqB4rzkWQc2JNqrLClrg37WmMojIRxyoF9HbvMFSKO3mWF2K+6RDFVkK9b4jEQ5qT6Kdc2HqsMUSiU6vKphdUPVewn7m+tAsCqCbxOhujPH6/FrqYOvL9slygZTlnQbsI3XOsMUXrrYJFq/x+Kt9+/ap61BMwDokxNFZxmH/MICoi8Rq9vipWrVolirPDPxdtxxD0fY/GWfcqEi9UZad1U0sgQFaEL/SuL0bu8CKtqmnDRE19hS22D+D9WgYf02YRYm1j0C2Bkn3KEQiE8eMlYDO9dhh0N7bjjneWGL7GnuQO3/HMZnvtiE7bs0imSlVf5eMmcjYuoWUDkRDqksd4WBEE2U5j8g6GIhEMY3kfc3tZWVijsrqnCpt3iZzhuRC+EpNWq/aqKURDSuOBI+4VljlhAtLG2FYI2COxnYqxgZjrBAtR4u7h6KUvmpGORC4jmr6vFVS8tFOU+RjBXo36jjf+Hhx1zXW14dM46vPrNVvzwoc/w9Ocb1LLPVpMsbDqwc7V9n1x31dTRhYuf+goTn/ladFbs5DJEAGf/uou7wBpMhqXv8Km5K7GjoR1DepbijgsOwVjJBWzp9gaTDJFNyZxdMxFGtAg490/i770P1GnSK8mfpGzG8F5lKIyG8fPjhgIAXvxyk3XvHdX7ifuGTZjqWmNobZHGPAuXuZC0f5ntdhwWNUQ2+hDF4kn8Y7Eoq5koZXkO6i+OrTW1dcp2yRkiJSBiMrsJRwzAflXFKCuMoAg2HPeiSlDYu7wIpYVRDO1ZiijiCDHdvo0MURRxDKwWv583F4mfge8/dNwIpVZOMVZIL0PUlRSPi8KwgH//6kQcMsD4+qMERKz+MQPJHMMNyVzNEvGYKOsD9JB6B1UOFMfyZFzpeaPFac8mO3DXtwffW4pLnvoKY+76COf8dT5+989lmPnVZny1oQ57W6RFgHAIn66pxVUvLUK7lH1wnCEKhRTZ3MsTgAdHAA8MA+4fCjx9CpZuFI+hMQMrsV91sX3JnBQwFSCOXuVFGFDFB0Tq2tcCIQZAQDjEBURmttsMbYYIsO5FxCy3qyVDFG7Bq6MrgZnzpDHVaFFYkyFaVdOkMl75Yn1tqrlQZxO+3aRkZXY1daCzjTXbtvE9HXAmsP/p4jzoo9vM/9dscTxT222324vkEBQQeY1eDZE8WTMo4Oast5/6fAPq27rw6jdb1TUKQGrxYBo1REXowhFDqvHWtcfLAcz/zlqk+h9DpBMilOxCMhFDUTQsX4Qriwvw/356BADg/WU1KtMFnr8t2IpYQpzE7N23T/W64gbytts2XeYAzzJE32zah9W7mlFSEMElR4mD6XBJMtfGCoVdNlXYsqcBgHpVt39VCYrYRUOWHoj7JSpN9o4a2hPlRVHEkwIEbX2WaYbIhqkCIB6PLBPD9hPnNPfnj9fik1V7cMOs7xBPGDih7V4mfaAx+o9rkSbGoXgHQtLktzWWwIwPVmP8X+eJhaqAsxoiO7BzVUjK5gkzv9yMhrYuJAXg0zW16sasgHrBwsxUAZAnB1+s2olwCHj40sNQXhTFoQPF4Grp9kYIvKkCwGWI1qiaR8YTSdz+9jI89NEatabd6SQJAEaeDlw1B/jp66mPSROTjnbx2BoqSa9+euwQFEbCWLK9Ec12nNXk12OryUqw0tAgnb8WfYhYjQLLjMSTBlJf2ajEuoboPyt3o641hr4VRfjhQWKAcPB+4nbU1kljFV9D1FYHJOKoa+nERyvEMfmnxwxBKBTCoB6lKAppzlfdz6MsVLHgYWivMhSDy7LbsN2OhpK4UpIvfrRiFxrbu1Lqhxjs9++3NUBII0PUJYjTiPJCoLjAXELFTBWSch8iNzJELpgq8HI5FviHw0oTayNjBU8kc0oQ8MaCDVi4uR4dXUmUF0XxgxG9cNWJw/HgxWPxzvUnYMXdZ+PVq45FaWEEX6zfiykvfovWzrg9i30tB5wl3nY2icdye72Y5aj5HnvXLAAAHDGkB/qUF8k1PoJN2+0oEuhVXoj+VcXy+S3Ita/KmFiABEb1r+QCLj3JnOaY0WaIAOteRCxDxBwiuQzRK19vwZvfbNR/L/lzsYBInNeJDZuBwoh4Lsxbuzdl7pFob8SizeJ9hVHx/5qbWUBkozFrKASMv188x9d+AKz7j/H/mkrmMjRVCKjlNkABkfdElRXtPU0d4sTFMkNUDQDYvWcX1u0RJ9nz1+2FYGSqwC5iTjJE0sFcHIphaK8yDOlVin9eezwOH1yNTm2xvhHchagEMYzoU66SSYwZWIUzDu4LQQCem78x5ekdXQm8ukBZeatvaEh5XZXtNjN7sOpDBNgMiJxniFh2aMIRA1FVKu4fliHqbDeRmqWDtP931YuDmyogqiwylMwVhOKoKIqiqqQAB/QrByAgpP3MrDlr3bpU8w4zXXNBCeSmvkzuFi5QBlkpO9LWtE/MagBYsr0RT89L/f7FDydlD21niJTvrAQxnHRAb/zp4rHoXV6IjbWtmPzCt3h0zjpnNUR2iBYpx2LbPjR3dOH5LzbJD89bW6t2gQTUF2ULCYbAvjvEce2p++PoYeL+HNW/AoXRMJo74uho0QRcvUaKtTKdTarmwTO/2oy/LdiKxz5djw+Wcxr6dF0QBx0t1hRpkV6nQBAn2QOkxZDe5UX40eHiebNbcqBzYrtdFFICohZWq2ORIYoK4jFcFBEDQDZJT8GBZI5leS45ehAKpInOwfuJ+14eqwpKpXE8BEAA2vfhn//djq6EgLGDqjB6gHgsDOxRIksBbWWIQkpANLx3KYp5Zy7TDJE4FkSQwIkH9MZB/SvQGU/i3SU7U+qHGKP6V6CsMIKWzjjijdLx4iQgkoLPsgJrN7Pe5YUoLggrQa8rGSL7AdHG2hZc/+p/sWKnRtIrB0THqO+3Coi8kMyFQhAiyuLAgz8Zi09uPBlL7zwLr19zHG4//xBcevRgHDa4GmVFURw3ohde+eUxqCiK4ptN+zD5hW/RLmceHNgiHzsNmP5f4Nqvgeu+Aa7/Fuh/KABgx27xcx45pAd6lxfJNWqG5xkjwmWIyorQq6wQJRHxuYmwdB3nxoZCaYHW1MWOP2aKKvXltHLLDIMaIuYwp5MhmrN6t7lkj9/meCe+Wr8Xn62pRTQcwr0TxOvYvLW1SGoyMNtrdqG5M46K4ihOPVAMoNvZmG43cO1zIHDcteLvH/wu9drNMJsLsnlnvMPSsW5LXSvqtSUPlCEi0kaaNCQ623HiA59i9TZOPmO0ei1NqNZsVhyCaps70dxQp3o8xckqzQwRW9ntWVaIB34ylhsMLAKPSKHszlKCTuzfJ3XwZfr5fy7eIQaEHP/+fifqWmOISEFUS7OOPEbWgTehgTXzyzRD1KbZj2ZwGaIdDe34WLLRveJ4ZYLITBWSrttui58zlOyS64cY/atKlEmFxlShEF3y5PTAvhUoQpecTZEHscqB4r5NxlP6bCTNMkShkPIa+yQdc8V+yqqqtE+37tyJpAAUF4jHxyOfrBX7t/DEWoF9UqDU71DL3QFAdTEsRScO6FuBS44ejLn/c6os1Xr+i00QnPQ2sgu3svbSV5vR2N6FXmXid/TNpjoktc4+DjJEiZB44S0KdWH6DxWb4oJIGIdIk/COlgbxTjbRixYqEzapjmjbvjb8+eO18vPv/PcKNLZLx4mTAl47SN9FMWIY3LNEPo8ByNkJR25iUeX4HSGNJR2tbEwzMlUokJ4TR1E0jF6lYsDTJRhMzm0GRFvr2vDFevEYmjhuiHz/Qf3Ffd/c3CDeUVguyvCkSYbQshuzpCauzEwBEKViSkBkJ0MUk7MpQ3uVoTjE5HYlpkY3giQJjCKB6pJCXHzUIADAPxdtweo14jFy3HC1MiESDuHwIdUABITb0pDMSfu6rNA6IJKzZXb2hRHaccnoeNYJiO54ZzneW1aDW99apmRPBUFxmBukDYikc9HIWIHVjbkpmQMgSMFCdRFw6bjBGNm3Qrcmi3HU0J7421XHorI4isVb6vHqfCnz72TSGgqJ40m/Q4C+B4mSXGlBqaFBaeZbUhhBaVS8nnQkrQIittCTQO/yQoTDIfQvEz9HF1iGSDkGSsJxHDqwylySxx8zetkhINWBV4s2QyTNFzqb67Bocz2XxTKvIRLinZjxgXheTTp2CH58xCCUFUZQ1xrDzpqdqqds3SmqTMYN6yln/mPtzDTGwfd08v+KAfi+DcA3T+r/j1ZNwFNYrlyHDLJEdS2d+N0/luKUP32Gkx78FM/N34gupvJwe56TQ1BA5DXSQRNFHIlEFz79rzRQRYuNByspA7R1h3hCsTRsU700+Mo1ROJJL7TsxsQnv+AaNzqrIRrKT7Qri2UXmKRV4BEKyRej0lAH9u+TOmk5elhPHDW0B2KJJF7gGjYKgoAXvhRX2K8+aQQAINHBJGepkrnW5ka8wdLYTgKitn145/sd+GzNHuUCyCbhPYdbv46UIRKaanDHv5YhkRTwgxG95FoCQFz1rCiKohhSoOuaqQJbXUuo6ocAqYZIG7jKkqM4BlSL3++B/StQwrYLUPZtKKRkiTjZ3MwvN2Hu0s3q/9XCBm9W2MlflKRjt2a3uDI3cdwQnHFwP3QlBNz0xhJlUJXfVxAHd7uSk3BY3q7iUCf27ysef5XFBbjzgkNQVVKAxvYudDVL54qdPkR2kVYR2xtr8ZyUHbrj/EPQt6IIHV1JxFsbxP9LWbDYZZkh6oI4Ue9dEkqRHY0dJL5esl3nIsfVEQmCgNvfXo72rgTGDeuBEX3KUNvciQc+lEwX7PYhsot0nBehSzYWYYweUIXe5YXOJr7cIs3Zo8V9l+iwcBLjgqj9qopRJsU7MSPJnE4fom821uG8R+fjhPvn4sQH5uLkBz/FRU9+BUC01R/cU9lfQ3uVoaQggoJkh3q7pABi9foN2Li3FWWFEVxw2AD5eQOrS2zWECnj8kA5Q1SmPNfC0p+t2EeRQHVpAX58xEBEwyFcsOtx3L3hEhwTWqXKNDOOHNIDlWhDJBlTfR47xKRJcblNw6lBdrNlRmgDIMMMkbKYBojf85frxQng0u2NmLdOWjSp3yxmesIFitsaw6wXkcD1p3NTMgcgIY39+5Xbn6IdNrgar119HKpLC1DfwDIPGV6LpLGmRGhH/8pieaGtks2n4zYzRKE4epWLT+pbKp6DMUE6F8NhJEPiiTuoMoJ+lUWI2q0hMgqI5LHXqIaIZYikRYtScb6we/dOxJMCCiV5azJsMNeQxp2augYs29GIssIIpp9+AAqjYfxAcobctF3tBlezR1xsOHZ4TznTLFi5aOpRXAmcebf4++cPprQEAcAFRDqLvqGQobFCPJHEy19vxmkPfYa/LxL3UUtnHH98bxXO+et8fLl+r/0+jnkIBURewx00xYhh2TppMl7ay3ilTwp42pvqEAoBvzxJnLjHWqRsh+wyJwZEoUQntm/lVrBsSOaSEWV1l2U4AKCyJIrSiDhpZZM0U6TPV4IY9u+rf1KzLNGrC7bI1t5fb6jD6l3NKC2M4NpT9sfA6hKUhKRJo46pQqK9iZNZ2DFVEE/4PXt24YZZ32PKiwsx4fEv8emaPRDkgGiE9etIGaJQVysWrtmCwkgYd1+olneFQiEM683p/F0zVVDkBtpJTP+qYnnQVvoQ6WSI+pWjBFx2gtdky3VEorHCxtoW3PfBahQJFg0gUzJEfEAkHpv76sSA5OQDe+O+i8agurQAK3Y24fFPuYnFbkkuZ7d+SPP+pejESC4Ij0bCUt8WAeF2aeXL1QyR+B18tWwNGtrELMYFhw3ASQdIkyFthpYPiOQMkf6x2ymI32EfnUOHGSuEu1hwwJ1nnNPcu0tr8PnaWhRGwphx0Vjc92Mx6/baN1uxcPO+9OoKzGCTd0l2q2VQj1Jn0ig5uInjjIP7iestCZatNKghYsd8KIEBlUUotQyI1DVES7c34BczF2LFzibsaGjH9vp2bN3XJhesTzl+mPrtwiEc2L8CZWABkbQvpePsv6vEcfhHhw9AeZFyrok1RHYyREpQqNQQlcpji2AxtnQkFIMDZqv9w4P6YnR4MwBgdHQ7juDqhxhHDumBPqEG8Y+iKkeTHSaZK43aaAAKKSCysy+M0Ab0RgERu062idfNv3wiZk4rpO/l8bnSWLR9oXi732Gpn7u3lCHSc/XqbOLUHi5K5gDEQywgsrdPGWMGVuGCsQNQGtIE7OkiXX8r0I4jpJ5VAFAhnUZtcYvtC6slcwDQV9rFHYJyfiSl/xtaGUVvrkbJsoZIz1ABSC0n0NIgmSpUqSVzjfv2yNsLADEjcxZpPNu8Swwopp2yP3qXi9t1imRmtGu3Wq7XsE8MwI8d0Us2HgnLvcUcjsljJwKDxomL4J/cpXrog2U1qK+XAh29DBGga739/bYGXPDYl/jDOyvQ1BHH6AGV+Me0H+DBn4xFz7JCrN/TgknPfYO/zZcW2DI9tnIQCoi8hpsIlCCGtnrpBDUyVADkVfYqtOKoIT3wkyNFS+JwTONiVVAs/+/+IXGVIBkutHWRaU2KA01pJC73tQHEyX2fEimlLdgIiKSLk5FkDgBOP6gvRvYtR3NnXG7YyOovLj5qEKpKCzBmYCVKoRMQSZO/gkQr505jP0PUsE8ZEJdsb8RVLy5AvE58b8FOQFRYiqS0ytIvtA+/PfNAHNgvdZAZ3rsMJUzW4tLKicCtro0bpp7E9K8sVvaHxlShMJTAgCrxvlH9KuSLo6AddFlAtHslBEHAbf+/vS+Pj6us13/O7Fsmkz1pmqRN9xZKS1taWii7IIIFFUVRAUVA3K56veoVr+tVVK7LzwUviOKCXBUFV1A2BZStFAqlpXRv2uz7JLPPnN8f73Lec+acWZKZLM37fD79pEkmM2fOnPO+3+/3eb7P975dSKQy8PHE1HyRVunn03WQJjTipkSvTXtiFE67go0La1Bf4cEXt5Gk53uP7seu47R6WWz/EEVGSIiMSfjZS+sQQBQOlQZcpeohAnjQ+/JrJDj60LmLYbcp2Lq0Fg6k4GKsgaFgkQl3I8VnyJhfuzFaLa31ZgcYjCFys+RAxxCRhCjV8yq++Efi2PeBcxZjcX0Am9prcOUGsuF/+rcvQeWSudIyRKSokv2cLdViEpD/nk1y2WACi+sDWFQX0BIPS9tt7XnnV9rh5QlR/h6ifT1hXP3jZzGeSGPzohrc/4EtuO+mzfjt+zfj3htPx98+uhXnrciWQq1orBDWKnpcNCDuPE7Wt21r9DbyhfcQsSQziRaaEM2r9CJoJ3+bsuVe25mEyefQzDTeur4FIZBK9OJKBV5XdpC3tjWEGpD9JV3kPROnpK+/YIbIN0mGqECXuRCVNY8ew1OvdeLpg4Nw2W342XtPg8tuw7OHB/HMwYHs+UMi2B4R7sQH73oCP3/qsPY7JpdzVZSOdaVIUDlZva/4EG1Fk7CXTvZep2uNX4nqjDj8PCHK8/eCZI4xRLU+ssbFMtp1mFTI71qCNtQG3Fwyp06YIWJSd5MeouiwVrxiPUQ0HkuFSdLip31OMasYiB5DJhlFfYWbF60B4CxaIIuO9On+xJkag89lx6p5QTRVelDpdWrqjWKTC5sNeP3XACjAS//H5wn+9vljeP/dO3CEyvMsi+MGp7kdR4fw9tufxp6uUVR6nfjSZSfhDx88A+sXVOOtG1rw2MfPxtWnt8GmAK8dp7JayRBJFA1FQcpG2RglgSrQKm+uTYcyRJXKOC46qRGL6gJoqvQgSDc1bqoAIOkjG/EihcjrYvbCqNfhJFmM6jxqljaZBWXxAhKilJ3cFD4ljvZa89e22RRcv5VsLHc+eQh7u8N45FVyU7EK7EnzKuFXTJgJWm33Is519PFCmCuaECXHyA1/6xWn4H1nLsQCxxCcSCGuOvGL3fkbq1VVRWc6BAA4oyHB34cRC0WGqEQBJ5MUOJFGc0j/nKJkjksPhOCwJUgZhwo3at3UectuCD6aVpOvx57Fvds78NTBAdgU8M00K4GiiKgkKPOPUUMMcVNi1y7Gsa6tCn5ajb10dRMuPrkRqQyRzsWSacFyu8D+IYoEvZ/qPCnew8Nw1tI6VCvkHlMdvtIGKlRGVJEcwMJaPy5dTSRRZyyuRVARmlMNDFF8qFObsWHBEEVpgaLaJD5cVBdAwKVoiaooiaWSuUT3bvSPxbG4PoAbz9au0U+/fgVqA2509A1BUWnkWqpzwnuIklhQm72ht1QVKBOjGEuRcxCwZ1DpdeLkZmFNyGOqAADzKxzw0kQgns7dQxSLx/GuO5/FUCSJU1pCuP3d67GmJYS1rVVY11aF9QuqTQsfAA04jWsVuzZSQ6ivcGPDAn3BS5SJseDPDDEaCLuR5Pe8zaagJUjeTyLH3wJAlFbsfXYtITp7WR1qbGTvWBRSTf8u5HNhCf3dmFLc9cHOtbeAZRkgw1kn10NUoGQuUE+SJTWDX/3tSQDAlae1YG1rFd66gfRWfe+x/cDxHeTx89dnP4evmlfTD+19CZ/9/Sv47z/vRiZTPrkcoK3p9SYFknxY0STIpCd5r6s02QwYGKIAvc/GknmOjzI8TqQ4g1JDl4KIkBAloEnmqv0unhBF0yYhaiEMEduTooP46h9exH/cuxPv/8XzuOpHT+Mr//c38jtfjXbtUIbInwmjwuPA6kbqoJmxSog085N/O38pfC7tca01Piyo8aECtABFbfmDSgTr2qrgtNugKApWNgUFpnkCbEvzOm020ZF/4pXOEfznfcS11a9S5smSIaLr01gv9veG8Z67nkM0mcaZS2rxGO3HFXtCK31OfGHbSbj1ilM0xYk0VZCYCJJUh1rtTPFgLVdvw5hCLuIgIrhwVSMURcHWxbUIggZdghlAjxoCACy2kYRoVC0sax+Mk4u9xp1th8wWrGjGgi4WEANZGOYHVNPKI8Nla5rRGPSgNxzHdT8jEoVzl9ejnUqeTppfaV4tESrDLJkc0nszmIMmRP40keVdsroJn3nDStz7NrJQHlXr8eSB/D78v3z2KA7GSQB606k+3SIhQp8QTaDqaYJImpxPnz2ddW4rvU5u48s+SzE4nFdBbm1FUbCsmvxtDIbjmncqWdQiA/i/v5BN4mMXLOWBd1/c/PMMZ8j1zJMAE4YoqEQ0KRk9ji9tOwk1fhf29oRxy1/2aAlRkZK5KL3mFlbadH1VAFAf9GBdLd1MnaGinjcf4l4S9NYrQ/jgOYvhoL19NQE31jeQc5W0+zSZB2WIvOkwbAoNRC0CQPZZV7uzA1a7TcH6RqFSKrIlNYuhKjb40mHUYRhffdPJcDu0z63SR3qr9LbNpZE6ZARThQUmkrmW6uKYgNEkOZ/sHKyaJwYMVqYKWoIwr8ImMEQWL0J7iPYcH0T3aAxLGwK465oNOnlbPizXMUT0fVP2sAYjuPjkpqx1osbvgoeyZQNxWIKdg0qn/p5vob0kUTU3DcMkcx4hIXLYFFTZSHC2qtZ6jV5ZQ15jOFUAAy8gziVzhT1+fpV3Ui5zqlCZzkCxrlQrCu8THe3cC5fDhpvOJj1BN2xdBIdNwRP7+pDuoyYkrKfSgHQ1+ZuFVIVxxxOH8PHf7ESKNeyXWC4HaMxE7QRizmWNFXwNH1MnkHAKGMmQzydoi+GkZi3uYAxkOI9kLqNottssIaqi9/dYSrsWWfF1XsAGj9MOD5Xtj5qZqBXCEHmruOzwT/96Eb/efgwP7OrGP/cP4OA+Kvlicjn6eACoUsawdUkdGigzx9ZlI9h96EYSl5ySnZSdtbSOs7KMqaxARCd9XyH2905UfkaT+MTR7Xj/L3YgnsqQubRsb7bqJ6fugbE9D+Jddz6L4UgSa1pC+N93rUO13/r+X9taxa+tTKlaA2YQZEI0BYjSRenSVdWooglRzBWyfPzzvWTBqHdFeUPv2e0+OOgUdm6qAOC1MfL7TUGiBe1PeUj1Kg8GYmQhqzJJiKp4QpT/8mALbltF7oXR5bDhvWeQzaljkFQv2PcAYYhYkBFRhAXP4UaKNlyyczcQzXtYfIEL0QWONapXxUij4GG1gVuaW6FjMIL//vMe9KjkuephnUAtrPVri1uJKicRWh2rMImBFEWB10YC//4o+bwzgrxgnqA9b2dSetWw0DlcXCayKvESVjQFccNZixCwkWDl8Kj5dTSU1B9QxKMlPikXWYCDiGDrEn3ltCbgxq1XkOF/Dz+9nczzsTk1J6cCMZYm76PNovi1hfay96sWD5ggnuwm12GLM4xta+bpfnfGfHJOxhRhY/NWIWWs6FskROP0s65ymUfya1nCpeglsRm7B50KSbyuX5nKYiYA4JLVTTh/kZ/+vdNclz8BDCaow6SS4D1rIoplAkYS5JoN0XNwUiEJkc3Gex3nBWzw0PglZsEQpelakkwl0VLtxc/fuxFVOQIAMyxvCsJHj4uxpUkPSYhqlRFcahIgKYoCn40UMHpyON0OJcyvg2b69scyuY91nAaoXiEhQmIcdjqnqUKxzsaWhMjf9sYL1L5RxGm7R6EMkdhDlFCKey0A6BeOLwZPbte9atK/ulDpxjtOa0VjJdlbWqp9uGxtM+owDHtyjLilWkioD2ZI0H2qvx/feMtq2G0K7nvhOO55lM7rK8KAolAwxrjGUzxD5HM5UOMi11pXZHIh3pEx8vdNnqTO7IUnRBauzwwsYXIqaVTTjLmKLgVjSe3Y2Ptt8FPJJ93bzBKi8bR2oR1NmDvFpjIq+lTyu7evdOETFy7Dl7atwgfPWYxmhcjiEgFB1koZkxDGcO6yOlR72WuZnz92D/tsKQQ92dfw1qV1CCmkCKFWsYQoio2Cw+NJjV7NXnyiMcO8UwEAfXufwtHBCOZXefHxC5YigDwM0UlvJi979AmkRrqxqM6Pn1yzQcd0maG12oeAjXwo4XRp9pGZBJkQTQEitJqwodmDdh/ZSA+OWwcIT3SQC67OoUX+p88jF19cdaCbMrF7ukaxL0oCnQUqcTQZTnuw32IIqojeGPnoQ47sAYUhemhW1RERo1TmMr8Apd6Vp7WgwkMev6yhApsXadWSugo3Kmxksz40rA/ExynrVUtlH73R/AkfS4iCiOD8FYI8cYAYKhxRG3BkIIJEyjwAVVUVn/rdS4gk0rBVsllEFo41ADFVoJK+SJ4qbqFgcoQKp/kxeihD1E9jxoFIikst6nzaRrqgkvx/NJ19XIcryPDc02278dU3nQyn3cYrQAeGzc9zf0K/EP7tqPZae4fpdWUbxyqTifXnLK/He89YiOUK6bVI1iwtbK6UgGH6Pub7LZKHanJejsZ8BRUHCsUjx8h7a/eMcXaIYX0TOe+DQkEilspwBpdBteh/C9NqadDiVJxcR15vHPrE4y+7urAvSQKyq5aYDxtVFAXvXEeS05gxKc6DkWgSH77nBdM5Yj0Ren06UqbMaUuVh7OYlva1AlgyEKTX+6o6B2fWBnOwFkxu0+BX4KGBmlVC1DFCAnGXksEv3rsRDcHiGYpKrxNVDvI8x8bJMb80RI6v0TGGtS3ZpgUAOEPUNW793INxxhDpr+1GGi+x9dYKEc4QCX8vjh5IWO8NS0Lk67GIHft6ChhmShFLZbNSuVDtd/G5Sr3R4gP+QyPa64ypbqRz3OMdIMnMInsPbjp7ke53N529CItsZE1PVLSYJu3pjIpH+8g6dnbtCK5Y34IfvXs9vE47hvvInhv3lNDJkoIVw8wY40LAEqKOcPHnl0FVVewbJv+vd+vl5SzhDidyH9+AsFe7aPBfSZP9cMqGVDqDRCrDY416n/76HTFJiDrC2jr3lyPZvweApw4OoCsTAgC8f10AHzhnMd51+gJ87IKlWB0g/UM7w9r+1J0kN5hTSeOchV7U0PMeTprHQF1j5Ph8dvM1d1N7DUIKudcOpcj1EbRFuEEOAKwU2ForeXpezCP7d1PiKKocCfzwnetwwfI6BKikN+k0T4gigVbsdSyDHRm83f88flZgYchuU9DgJefGSkEymyETojIjnVERpgHcPD+wJECCzZcGzTe2sXgKj3eQzcKf0TavkI2UJEbhwxN0Psb9LxxHLw24bBHSwBeGD88fMZm9Y0DPOLmoK8wSIrpgjeWz1ITGFjT58k9+r/A48e9n1uLN9sfxsbPnZ8mdWOXh1UHtuXrDMYxS2r6Wyj56I1Z6GA3H42Rzsykqzm0TNjrqMNdlb0Y6o+LwgHl00jUSwz/3D8BhU3D2BsJqWFp4ggRJPpoQHSs8lsiJME2I/A7zTYdtML3j5Hx0Dkd5tdzJTAUANFPSwsjsRBNpfO1VEkyf5dqLNc1BQFXhpuYArw1kf6aZjIqeqP66+MnLMW5p/tRxsnFWKhHLuRn/cdEynF1J5Cb/GmvMGdCYYShBFuImn/l10OYlhYSedAC7jAMYJ4iRaBLP9NEel2R/1u+XBMmxDGa82EPnLf12xzF0Z7QKZlK1Yzhqfp8wqVTQIvldTmPskYyHW5enMyq+8/A+HFHJZ+gbO2p5/ItD5JyNqW6MxpKWjxMRT6Vxw8+34w87O/G1B1/N+rtOuoYEbOa9eEy2CQD98fyB2QBN7INOco4q6HqQURXs6jU/5mgizeU29T4b3PQl4xbL0Sh1APA5YOqMVygqHeTYDtN7/ZFj5Fw0O8KW1z1jyzrHrK93VtyocOrfQD0NQoYtAjSGcXqa3DbhNURr3YR1NlblYAUdD263GqRsAnZJewosGCuKAi9PDvOv5Ua8NqT9zbjqtlzDVVXF/R1k79hUOYx6Q/LbXhfAZS1kX30tbS69emh3D3aMk4JaG8j6f87yetxzzUnY5ngaAPBYT2mlQ8l0BuM0QaiaYEIUogn7oXDhf981EsVX/rIH1/30OVz07cdx0uf+igf3kXNb7dDr1HnCkkP+CegTImZ176fJVAIO9I3F0Tkc5X3BLPZwKdbPf2Qkg1czLTiSqccv96RMi15/ebkLvVTZYR/XjJVsNgVn1JH94aFOF/rC5AUePTCGGC1kVtvGUEnP+6hFj9Rxet16FfN1ye92oMZOXucPR0iiUeeIweXQ1sT2Sq1X+3g4fz+zEcl0Bve+lkSnWg2bouKbZxKXwSVCn+ArA+b3161/fQ33RIk65P21L6DZhOG3Qr2H3PDGOOBEwIn3jmYYOoejXG9a406jyUkW4Gd6FMRT2bv23/f2oj9FqgX25BiQpjdKdBgAMKr68cS+fqQzKn7/Yif6VH01MqzmT4gyGRVdNJjx27NvxKCTaXzzXx79tErAbpJ8uDr+K/yP84e4MP63rN8xu+dX+rTneu7QEMZoVdyTIYtz91j+TfSR1wYRpsxStU3YMGlClAotAADs6zGvmO7uJEHt4voAahrJYzHaafpYBtancaSITSgXwswozWH+fpmpQo+QEDF3InHOCkschpIORBK0Yq+q+Mz9L+OhkXmIwANfepTYb6cTsNGG1lf6s6+No4MRjKa1SlJUdWFnn4qnD5Kg67Gj5By41ASQjGX9PQC4HXZcPm8YAPDEaCO++6jF0EMTpDMq+uJk86zzmJ8XB7XcHlCD+MfePtPHFIunDw6gm1YcbclxIK6/bpzUEjus+vD4a+T+vOPxg7xgAZAAoHMkW+8ZT6U1NtDis250J+nze7G3m7zWX17uwr7eMfQ4qERr6JDl8VfYWbDrxqtd+TP2DDW/YJ9rMq3iMWqEwtBBJZVem3lQ4Mxo5d2jo/nv2X4q42UOT2yg5jg82NU1avo3nSPaNR+wp+CmwVbUgiEapRVtt62w9coKjM3eN6wikkjhwUPk/QXSw2Q+jQmcKjkfYoXbiD7KmAQM63ItXV+HEvacBQQtIbJgiOI51AOUPRqDB/e/eBzdI4U0a2pGDp4iCsash6hzAmvlHqFQE4EHr3WbX887j43g8X7CArQp5vbLr6snBZOnR6rx/JFsSfSdTx7EQZXcX/bBA/yzXfPK19CKbnSq1fjs0VO5TXsp0BeOI0GT/IDFepAPflqc2z9U2PntHY3hbf/7NG5//CAe3tOLV7vDGE+k+d4btOnfn4deX8Px3M/fHxWOnxrL2Oi6kFAd6ByOoWMowu9hhT6G3Z9Dsez3f3QwiksT/43zE7fi6EgSzx/VxzvJdAYP7upGN4uNOl/Q/b4+Q9axw6lq/PAfxDH00Vd7MQwqc4kOcYZ2JGnTz86j6KDrGZcEG5HJIJAh1+VzI9S6XNGv/a4M+T4KN/YUsCaTp1Xx3OFB3Hz/yzjtvx/Gv/9mJ17KEObznIpjAABbgjxXXHXi2Y7sYkE6o+IPO4/jz+lNUGGDt2cHMGi9dxhR7SKfTef4xNnHmQqZEJUZB/rGeA+RPR2HP00W4O6UnwcbIh7c1Y1RCPRpbET3dRR+PLm/H08dGED3aAxjLj1dPwYvduRJiHrCMYylWVNgNidd4WR0ce4LPpZMo59W62sseh+yQGfeZA26Sye5VfKLPdoxPXtoAGMGM4DOcP7Xemh3D0ZAK8A0mUQmzYNGdz1plN1v0Ue0hwZgK5qCQDCHhSc//hRPUA4NTy7YYmA9FT67ReBPzxej748PR7l8CCltAwswKR/c/P3e/cxR/G7HcWQUB+LzNpAHHn5CV0He1Z/KCr52d41iXPg8Iu46AAp+8fQRjESSePp4Ahk6uZ5fuybwD5FhsK+qrfh/j+zD0wfNJ2YbcXwoyvsoKu0WmxGdrTCoVuAfr5UmIfrX/n6Mw4u4jVbSjPMt+P3pwxP7+vDXV7pxeCCCEbumGU/Aic7h7CCzdzTOAwKPBdvCNrkxePHy8RHCDj1CEskVKymDOXTY+g3QGURk8zVPLkR89YE9+NNLXXDYFJy5hFTIH9ylv/5Z4u+GRTBIr8G0quDYcJ5mAwB9gi6fHDO5FiNwa1btBnQNx3jwqKSTnBmJpswDNVZxdioTCzQZ2BDmVwczePTVXhxPkrXGlkmYX/eZDOz0fu3IkRz2Rshx+wzXQZAmSJGME53D1k2UY/SWcIkJUURkiHIkRDRZCoWqkUyr+Mk/CwuSommWZBaY3GQycNJA8uho8Wvlrj7tvo/Ajb0W8r7thwdxRCXMjz18TLcmMlRHCat6UJ2Ha378HF7sGOa/e7FjGM8dHkKnrREqFGLVPN4H7PkjsONnUKHgB5X/jr6UDz/91+Gi34cVukdjfD2wZfLfN2bw0h63/cNpS1k4w0gkiXfd+SyODkbQWu3Dly47CXdduwGPfPws/PRG4mJmN1w3LOGOpG28yGaG/nHh883Qx9GkJwEHukdi6BiM8nuYzWtjvTVDJkvL0cEIknDw3uL7X9APQH364ACGIkk87tpKfrDzHqD3Vf57hQ5lPa7W4edPH8GRgXH8c38/hlSaEEUG4ad7bkJ1oGc0e81m1y0rcmQhEYYN5Dk6KIPvSY/piyV0fRuHhxdgc+GFo0PY+o3HcMUPn8Ivnj6KoUgStQEXPG3UHZG5JdJCUhhePHsoOxbcfngQ/WMJJLx1UNvPIj/cdW/e12cIOZkcs+A/mTWQCVGZcah/HDHQinoyCkUI1h7erQ+qYsk0Hnu1Fyk4kHbQYD42rPs6pgQwOJ7AVx8gAeWqpct0zxGGDwf7xzE4br2QHu6PIM4W3HT2isOqUmMpe87F7lD/OG8qZk3GecGGohrZFiEQ392f4q/7zKFBjBmc83qiGWLbbIFwLImnDw5gWNUqPvw10wnA5kRdM2mg3ddrflcz2dOKpgqggvYQjfdqjJ0RKS1IOTA0uWCLgQVvHpv589kMCVHncAwJ1r+UFj7/JK1EqS7s7Q7jhaND+AKdWfPJi5ajauV55HGHn+RTqBOqHZGUDUcMcpQ9XaM8wQcAbzVpTP3rK92474VjSKs2jDPbXquEKDHOr4NFJ21CRgX+876XuewuF/b3hRGlCZktZREYjhNJ2wCC2HF0CCOR/BKx4UgC43Hra/2fB8h9m6I291nySTrXIqz6sP3wEL5Lhz7Ob9WMQxJwoMuEIeoNx3giq6QtjpVtcqoXLx0bwZ9e6sT+3jFUep0453Q6P2XwsCU7ATqDKApX3oTozicP4Y4nSDD8jStW45MXkVlHf9/bh2hCu+9Y4u+0CtpSZE1IwImOodxOKKl0Br10CfHyhIgyFqoXu47nZ4iQjnOGKGbxUbKKtmuSCREbGLu7P4U/vNiJOFyI2+maPW6ShAvr7OEcBRMmQ/QYpDi2NDk5MbgsJWKAxhDp3l+0wISI/m7tYnJP3/3M0bzyylgyzWc+FdpDJJ6LQphDEcl0Brv7k7zoElE9eM0iIXqxYxh9qETC7gPUjHnBYIAUFdwNSxGOp/CuO5/BTpoUsb65C09pgxJqJY8//ATwhw8DAJTNH8KWC94EAPjZU0dM149UOoNbHngVdz9j0exigp6RmDYUPTWxhMieJvdbOO3GwX7rzzySSOHau57F3p4w6ivc+MV7N+Jdm9pw9rJ6LKoLwO0PkQcarhs7VRGkYUN/DmeF/vGkNr6D7Uk0MU3Aia4RMghZvIcB8MGsg9Hs6+PIILn3LqFjD/78cpcu6fvzS2Rtbjr5bGD5JeSzf+i/yC+TUX5/1rcsRiKVwQ0/fx7RZBoRO+0pig5xpoqs2dmxDVv77FZrHy3CxuBCPzV3UNQ031/Jk9M1WS2sSHX3M0dxbCiKgNuBN586Hz9/72l4+tPn4exzLiQP6KQJUYw815jqxfYjg1mSwgdoYeuClQ2wnXwF+eFLv7HeOwxgrQ1Hw6opezabIROiMuNQ/ziiLCFKjPPNaVCtwMN7engQGE+l8eU/78Z4Io2GoBs2H6V7GbtBg0t3gPz8FVpROGud3rLYRRewF45as0RHB8d5QsSCFt1zUKYjCQd6R62lAAf6xnhCpFgFpyKSUWCUVnNGjxl+RxaKFGyIqQ7s6RrFcCSBvT3hrEbyuOrEsSFrq6bHX+tHMq0i7tQWOADAIJ02XrUAixpDAHIxRGSTXdEUJHMEbA6ysFpNvhbkYXsHCuvRyAcWvHnN5D2ZNGwq+fkxKsHpGjFniLRg2IOnDw7iprt3IJlWcdGqRjJXaQGtpB1+ki/SbNaPMdjY3TmKCLSEyFczHxsWVCGVUfG1B/eSl6ZOc5YJUe8eACoQaMC/v2kLPE4bDvaNWwa9Ig70jiPKjAGSFtdAhCRE7mA9Mirw5P7snh8Rr/WEcebXH8Ol330SKZMFvmc0hv29Y1AUwB2yYAvpe1U9lUikM9jTNQq3w4ZTVmhWvgnVieMm1f0egSGCSYECAE+IxuDFC0eH8P8oO3TdGQsRaKDN4vERvTxK9/fkOo+qbuyxkBgBRIb35T8TFvc/LlqGy9fOx6p5QTSHvIgm03h8HwkmMhkVB1lQkLYohtBrMA4nd5a0Qk84zmd+ONWU7pgjcOPoYMQ0se0a1qrpSMXhojtaJKWYJtgjVILjmExClEnDRtfNoaQLD+1h9svUVXGsN/tvhHX2aDhjep2NRJMYpsYSWVIcur7E4cLhAeu1j8WmLivJXI4eIva7xfObsKQ+gLF4Cvc8Y92Xxo45Q8MIZ6EMkXAuDg8X1ztxqH8cyTR4kXEcHi4hNYKwPQoSQVqUYMU4fhxxYJi8v0+88xKctqAa4VgK77zzGTzwchcPHN97xkKghigK8IePkD28cTVw7s143apGLKz1YySaxD3PZp+rbz+8Dz/8xwF89v5dvF8lH7pHY9qsvfTEEiKFMsKRHIxwIpXBjb/YgR1HhxH0OPCz956GVuOAZebuGA/rAmZWuEmqpA/ICgPjcaRg178XQ7IhSubYmsHmEA2YJEQdNCF62/oW1FW4MRxJ4nGqBEimM3jwFfK5veHkJuD8L5C9e99fgYP/AEaO8fd1w4XrAACv0uvHFaTmS5FB4RizGdlEKoOjI+T4bGrKvEhK77m4I4gI3EizUDsmfBZJ7TPaXUBCxFikW684Bf/z1lNw5pI6Yu4zbw15wNBhcuxMaqz4MBxJ6ky2MhmVM/2vP6kRWHEJGdfRvxfo2ZX3GADArVJL94wrq2A62yETojLjYN844iwhCneRoBpAzBlC10gMr3SO4kDfGC7//r/wi6fJgnrT2YuhMGttzhCRgKuyWnNMaw55ceriFp1lY10N2ZRz9REdHohoblMmfR4KtWhNwpwuZjjQO85nwjBJTk4MCVUyC4YooXgBKHj52AieOzwEVQVsHr2FXVK143C/9es9tJvc8L5KGqDwhIhuiNXtWFJPdL0H+8azgpNIIsWrsCuagmQqdIA23VoZK9DFLaq6cHiwgHNRAIZoI7pbMUmIhI2yeyyNVDpDe4hMNlJhc/ztjmPoGomhvc6Pb1yxmhhbNJ1Cpq3HhoGOZ8mf04G7e7v1CePuLn1ChIpGvHMTsRWNUtbO4QuR37Fr14huMjwODatQ4XHivOXENvqPL+Xu0QJIAhvJd82NEzanvY0c1z9eMwlQKcKxJG78+fMIx1I42D9uKrH7J02oTm6uhIM5DmZJ5shmVVenWfBesX4+Kmrn8+/jcKLLRDLXPRITJCN5EiLVi1e7wzjQN46Qz4lrtiwgwxfZ9WmlBadS0WNqLfZ2j5r2oWQyKj57/y6oKvCuTW14/1kk0VIUBRedRJ7/r3Qz7RqNccczRU3retY4UiyId6IjRwEDIFLIOGfJ6DmiVekUZctfMTHI0BUB0gnOjKRUG8YT2ffNUIy8b1aBnhCERDwCN1QVaK/zw1VJPwMzhoh+rinVhkTGhm6TdfX4UBRxui7bjIFwSmN5j/RbByGjzOVcXDMiBSZE9BqzuQN8APWP/3nItNeVYSSa5MGekinwnFLWI60qODJUXMDPgtcEla5G4CH7mUEx0D8Wx7GhKCliNNBkZuCA/skGD5L92FUBX1UzfnLtBmxYUIVwLIX3370D6YyKzYtqsGpeJVBLRwMkwmQOzpt/BDjcsBuGjotMxZP7+vH9vxOmOKMCD+yyNuURQdaDPAWSXFBVYT8y7xnMZFR87Ncv4vHX+uB12vGTa0/D8kaTmTXctlnVXztU/paCPWeiNzCW0NiutF4yl1SJZO7YYCRr33KoegdVhnRG5cXQhXXacOzf7yR7x1MHBjBMpWSnLawGahcD699D/vhvN2ssYWULNi2qxRmLtXgqVMOGuQ7pkjZjEevYUEQ/D8zsM6Ixh7uiGm01fqSZ21tcSHwSrODjwdHBCMI52NhEKsPVLFnurd4qzTK+8wXtNehn98whjSF+oWMY3aMxBNwOnLGklswNXEoZppd/Y/n6IhSqOInAnRUfzHbIhKjMONQvVLRZdcJdic1Lyc335T/vxiX/70ns7hpFtd+FH1+zHldvXgB4QuSxLKikTFFDveaGc9naebDZbXwAJADMayT/z5UQHRkQGSKTm1lYDHpzLHYH+sa0hMiqWi9CbPoe79O/Nl1sMzS529U5imcPkcA2WKnvk0rAaSkbSaaJph8AamrpeTFJiJpDXnicNiTSmSw5z6vdYagqsQJnw+R4H5GVsQKTpcGF4UgSQwbJ4lg8lTOwMEJVVQyy4M0sIRLOXZxW6Y6L1XKdZE5L1gDA57Ljf9+5DhVsfoLdAbSdTv7/2oPkK/0cRIZoaDyBrpGYTjKHikZcdFIjagPkuZ12BT72eVkxRGwgawNhN9nclj+/1JXXJnu/7pqzCO4oQ3TyUrJJ/OO1PlO2QFVVfPK3L+GgEGD+entH1uP+uZ9ch5sX1WqDAC0YotYmcs3ZFOC6M9qBCu3eNKs2AqSnzzSRFUGD1bQwVPV9Z7ZrnyEdQGlprNBLJLaHbC2IJTOm98/enjAGxhPwuez4r0tX6lwgWUL08J4eJFIZHOkXCj0Av/51oO8lrhaQEA1HtOdjMiEaMDg8ZGM3cwzsHInp1jI7TYjSsJkGGEyCY59MQkQT8QwUzlRcsnoelAAtwJgmRNQGl86lOmYiITw2FLFm7pOFSebCcZOEz2i7nbFgx5gsyhXAtjXNaAi60TMax+9ftC5UkISIMgBqoQmRxnb1jiVyyp+NeJVW0jN0GGvK7kU6o+Jgn/6cvHh0GACwuC4AZx1NiAYNCVE/NXOpXQwoCvxuB35y7WlY16YZFV13Jr2vGEMEAK/7MlCnSdUvX9uMugo3ukZi+AMNzHvDMfzbr16EqgLz6OyjP+Q4jyK6RycpmUtGAZDrwIp9ePCVbvzppS447Qp++K51uvesg9NLZjQBfA0CwBOiJOw5DSVIQmRgiFIa+9I1EkXHUDSLIWLqB10PEkgBJJlW4bQraAx6cNlakhA9tLsbY/EUl8tduKpRG41w1ifJgNLul4Anv01+RiWQH3vdUgCA12lHQwNLiAY1WZ/qyFqzj4gSP+GYdaD3nKeiFv/4xDlcuaPbE+k6woqPr+Zg7vf1hpFMqwh6HJhfZeIIR+cRoXMHT4g8VE30nJAQPUiT8vNW1GsDvE9+C/n68m+t1wYRNJ6IqS5LuepshUyIyohoIo3jw1Gth4jJxXzVOH8FCZSePjiIaDKNzYtq8MBHzsS5tFoOD7XrNUjmQlW1OKk5CLfDhjefSqvPwrTmtmYSXO48Nmyp7yQ9RCz4MGGAhMUgJ0PUN5ZfviTCWL0W2Rb69wqd2Lzr+AiepTdybY1+2GQSDhy1YGG2Hx7CaCyFKp8TtXVCxUd8/ep22GwKFtUR5sk4c4NJDFY2CZWYCiaVsqjy0QpuUiHB+iEhaNnfO4YttzyKN/3gXwVbTI/GUojSWRQuxYSSFyryCThwuD+C/rG4hWSOnKs4HXj7tTevxpIGw3yCBWeQrwf/DgCwecjnIDYss/PiD+jPi9thx5UbyAazYUE17EZ20whGzdOE6Oxl9fC77Dg+HMULHdaJvKqqhCFiCZkZQ5SI8Pd78tJF8Dht6BmNm242dz55CH95uRtOu4KvvZlM7n5kT6+u4qmqKv51gCRYWxbXaMUHC1OFJW3zcekp8/AfFy3Hglq/xtzAWo/eM2KRyOreFzn+iko6cNjnxLtPb9N+X5UnIeojcsZ4FQkAzGQ0zNhi/YJqOA1zlk5trUJtwI3RWApPHxzA4QESvGdAkybTdUQLfDuHY6YyMYbOYZElYwwRuYfcfrIWvmwiqewaFhqy0wnOUqRhw2g0+75hFWdnaszSBTEvaOJAAhny/i9d3aRJ5nIxRDayXh43SYiOD0etC1V0fYnlkMyl0hnuTOmARQ8RYL1WMwbAHYDLYcN7tpBr6vbHD1oWKkajSaRVeq0UzBBpPSQATGWkVmDyOLZPeP1kLTIGZswcYU1LCKDDWbMkc7R/SBwMHXA7cNe1G3DhqgZsWzMPZy+ljO/CswC7C1h1ObDhOt3TeJx2fq7+9x8HkEpn8LFf7UT/WBzLGipwz/WboCjA9iNDBb3X7hGxQDIBhkj4fK0czO59nhRnrzuzHWctrcv6PYeiaCyR2EdE9590noSofzyuJXdUeaIVXO040Ef6nROGPiPWHzuaVHR9i2zfn1/lg92m4OTmSrTX+hFLZvCXl7rwV6oOecNqYTiyvxY446Pk/0eeJF9DLQDIuvbja9bjZ+89Da6AuWTOyOofHYggDbsmgzNLiNjeR+chwsNk5NmSOaeXnN9cfURMLrdyXjBrXAkAoJkmRMdf4K/B9ornDg9CVVWoqsploK8/SbCZX/I6kjCOHgM6nrY8Bu24NXc8mRBJFAxWyVMdNKNnDJGvBucur4fHaYPdpuATFy7Dz41DArMkc8P85z9/z0Y8/LGz0E4DenFSdlN9PSq9TsSSGdMbTFVVHB2MIJajh4gtdskcDFEmo5IeIuY4VohkzrghiWwL3YzZ4rCvdwy76CLQVK+fBJ6AwzIoeJjq+c9d3iD0YdEgm0kmKL28pD7AX0sEW3xWiAlRkA1nzc0QpelnfYhWLGPJND50zwsYiSbxSudolnWxFfrHtI3EljGh0pnsAA4ACk8k0rQCbWaq8OaNS/CDq07FpafMy36+BWeSr3TTc9PP4VD/OGe2WKWxvlZg7GgyftM5i/Dh85bg829cpbGbLJkXoaoaQ9RIEiKP047XrSLP88ed1rKSgfEERqJJxBTGEJkEF5Qdgt0Fjz+E09vJsb7/F8/j7meO8Gr0s4cG8dUHiPPQzW9YibdtaMUpLSGkMqrOtehQ/zi6RmJw2W1Y31ZtzRDFSULk8lXhu29fixup3Ay+GqJhB5GOdY/GspJi0kOUpyJMq7OtTeT1P3r+Uo0dAoCqBeTr4OHsv03F+b3nnbcSQO6EaFN7ddbv7DYFr1tFksEHX+mma5uiXW9mnwUzVVCcSGdU02SQ4diQkAywa5f2EAWCJCF6xcRprkvXgB7nLIUVQ7QzWotOtRr2VATY/XvL48kJlji4/FAU4JT5laTA4KfrVI4eooyNXLvmDFE0B0OkJURHByKmhZXRWIqzNXYrhgiwNlZglty0b+TtG1tR4XZgf+8YHttrvm6JkjnuIpYP9L2x5NDsXFiBFTYcVEbtryDXhtFpjidErSFNTjRg2H/6qdNp7RLdjys8Tvzvu9bjO1eu1WZK1S0FPnUUeMtPSJJgwFWbyLna1zuG9/x0O57c3w+v047vX7UWbTV+bFxI7qk/7szPEvWMigWSCfSjJljc4YGq2NA/FtcVefrCcS4Nfsu6+aZPoYPLRO5F96RUvoQoHEeSJzssIdIS4pFokh4rXdNZckEfm1L1z8/6h1qriYJBURS8cQ3Zz77+11e5XG7jQsOw3E3vB4LCe61s4f89d3kDNiyoBnx03RMZIhPJ3BEae6Rt7JhN1jV2z7GEyE0L3HGRISL3m9dPzm8up7ndvEhbaf4AHUNE7oXq6jo4bAq6RmI4NhTFruOjODYUhddpx1lLhZjK6QVWXEr+X4hsjilOIBkiiSLAaHyfnyYuLJj21aAm4Mb9H9iCv/7bVnzgnMXZk96NQSWjWj2VqPK70FItND8KVWibtxKntpK/NZPNDYwnMBZPIZGLIRKCbSuGqHMkilgygwRbFKzkSyKM1WvThCiAGr8L6YyKdEZFS7UXlZV6Oj8Bh2UzH0uILlhZry1G0SFCBbPXr6EJEWVJDhgSIs1yW2BR8jFELCBkCRGVYd3ywKu64PMn/yrMyrY/HBekBmYMEbU0VsjGyazWbQ72uWbLEVe2NeHik4XKmYimU0iViMLpDSDocejkKGzBbq4XEyLyfD6XAx+7YCmWNlQIybyJZG74KNlYbU5dZZbL5l7usmTR2OfkC9DPxeyaow5z8NUCioIPnrsYFR6SQH/mvl3YfMuj+J+/7cUHf0l6BN54yjzOtLxtPdkkf7W9g0vsmLvcurYqeF32vAwRZ3YZbDYeKCdBzmdvWH9P9YSFoD6PqcL6pa3YfvP5RFYrIpdkbuAASRTcQTS3kGvfWDXOZFSuNd/UXpP1FABwEU1a//ZKDw7SRt2MPUdQwK5B+phcsrnj4gwtzhCR16gOVUNRgIP94zgsyBtHY0m6lgn9FpSlyMCW5ZCWSGUwHFPxyxR1VXzuR5bHkxOssusJ4L6btuBHV1Pbej+tMOdgiFjgd3w4+1wcG4ogznoTjFVnur6kFDcStF/QiJFokq8Zun6eiIEhsuojSugToqDHiTfTgLmghEgt0KiCvjd27eQyyBERjiV5cOrxkTUgVBkCAN0sokxG5U5xa1pCQA0tTox06FlBzhAJcrhccHpNkyGAnKt3bCIsOWvw/8K2VVhMe1XfeApx7suXEKmqapDMTZwhUpw+LKDDh1/t1vag3794HOmMijUtIa6SyAnGEIkzrNKCZM7CZS6RymA0ljIxVSD3ZUbRJvl6PLRoTNc/sY95QJCfs2SkVYh/LltDzm3/GHncRSc1ZsdTTi9w3me170MtyIKXJkSRQV1R2HivHR2k8n4HKwabFBh4QhSib9CEIaJF5ECQPCaXsYLIEJmiaTWRNoa7gP7XAAAOXxAnNZP96LnDg7yH7ZzldWQvE8Fkc6/cl1+mKfSnHR6ImLYCfODuHfjc73flHBEwEyETojLiELW7DDCJEdNY041zeWMQi+stFiQLU4WsgAvQ9SnAHeR6YLOEiCUSVUG6yKUT2bpRgS62SogO0CC5klZvTSvERjCGiNlYj2qVeHERZzcxAJy2oEZo7CRIqE4cH4pmSQKPDIzjyEAETruCM5bU6Ss+4S4SaNkcQCXZuLhkTkiIMhmVVyFXToAhsrtpQjQwjod29+AuOp/ivy8/CTaF9KMUUlXpGxMrayYLFNtUbDQhopp5u8ud/TdMQuEyOAiJsNmBts38W8Xpw7JGct7Z8bIFu7VBkFgIck0Odo2aJUSMHapbDrDkDcAZi+tQ6XWiLxzHM4fMZxIxt5zqEE10zVhJamsPPwnq17VV46lPn4f/umQlmkNeDI4n8N1H96M3HMeS+gC++qaTuQThklOa4HHasL93DC/QgOqf+wS5nPh+s3qI6GbmMdmw6N8oNBg2bhI9YhN1HlMFxRPU+tpEcMnc4ezf9ZH+IdQtx4p55LMxMkR7e8IYjiThc9lxcrN5FXJTew2CHgf6x+J4nJ4X0F6OXAyR4iSBw7EcTnOdOrmYvofI4w9yWY/o5MWkLKpd6D0Smr3DBu/toQh53t+o50K1OYBjzwJdOy2PyRJC4rCmJYS6Cvp5MKY+Rw8RPxeWDJFQqBL73ujf+wNkzTKTXg1HEkirNNARWWUjQxQ3WX9UVXtfbm1PWlRHAmqrxnk9Q1RcDxEc1ufCDGwdagx64Kwg10OojqzLIkN0sH8M4XgKXqcdyxoqiJTRVQFA1e4PVRV6iPQM0UTx3i0L4aJS08vXNuMKgX15/UmNcNgUbqJkhdFoCrFkRrCqnkBCxNZFlx/L6RouGiv8dgfZd99cCDsEaNeDSQ9RLskcG/2hFXvoNUnXOI9X24+8Pvr/FI1HaHKdgh39wrV3dDA7IVpQ68cpLSH+/RtONlFAAMDJbwVaNpFiHGNURPB4YUhjsVQnRmMpHdvM1CkpPy0ujujnIJHnGKZvjB4XKzbqTBVIDMWS+r3dYVNZsaqqAkNkkRC5/GRPBYg1PH3N0ygz+eyhQUEuZ1IUXbCVFBGjQ8Cx58xfAyCfIf3sHR6faf/e8eEo/vxyF3761BEU6Ds5YyATojLiIE8aDP0a7MbLBSNDxL6yn4sQGCK4K3AqTYjMBrQyd7aGGuF5jNVdzhDZLSVzrFpfW50jONU9Z4pbnGLBFvLVhCGCy4+TmrWbfuPC6qyESHG4kMqoWYEls1de21qFgNuhZ4hYMhZqJSYCAJY0kIV+f+8Y18gfHYwgkkjD5bBhYa3WwF4oQ+Rwk7/Z2TGMT9xLgq3rzliIqza24XUryed0VwFD/PrDgl2pmWSOV99JAMU2H4eLVdqyJXM8eLUCk80BgNPHGbS93WHEU2luUb5gwUKSWFbMy/psAAgJ0XD271j/UKPBLt5h4wyElWzuQC+5RhrYNWfWC8EYIr+WtAXcDrznjIX4xyfOxnffvhZrWkKYX+XFbe9cB79bq1IGPU7OoP1mewfSGRVPURnZZuZGxBii2LB2XlVV2+hMCxbkfTloMCwOZx2LpzCeSAs2u7nnEJmeb0BjiEY7s3tjaP8Q6pbxAKlrJIbhiHaNMLncuraqrP4hBpfDxnsfmZuWjV1vORgih4u8byuGSFVVHB+KCkmhvocI7gCu2khYvN88f4xXJDvpTCf2/EjrJXOjUf25HKAV5LSvDsqKN5IfPnen6THlhBBw6pBTMsfOBTlfZgmNTjIHVX8t0GvN5yNrVreJ/HAkmkTKKF9TVS0hYhbKZgxRMqoxPML7Ysl3roQoU7RkjjbO0/uho0BXTlaoWtZYAZzzGeCCL6Jm/ZsBkHM3RucAvUCLQyc3V5LGekXR7g+2D0QGtPWJ9RhNEvVBD7502Sq8bX0LvnTZSbpejyq/iw84zmWu0DVqvKYnIJljzLnTx2XfrACyu3MUe7pG4bLbSN9bITDrIWIMjmqdELGfqzaD/I/uTX6fltT4WXKUiun2uxTsGBg3SYgM9uDbqAy8NuDmSUAWbDbg3fcDH31Fux5E8HhhMGvtYnLfTEblx2CrorOpRrKNeLIkc3xPFHuItKJyhceBeCqDl01kwceGogjHUnDZbdYFdEBL8thruyuIFBBEeXGofxwuhw3nLK/P/lu7Q+sjPvqU9WsIe25LPbmejQXe379IEsSNC6vRHMoTc8wwyISojGDuVdWVhqzeZy5J0WEiDJHNATi9OGV+CHabgs6RWLZDCmWImgpIiBKq9RwiVuVqrKWLTz5ThdFjZMO0u4FmOllZZIiEhEisUJ+2sFrbyCkaq8n5NPYRMXtkbqVplhAxPTmAtmofnHYF0WSaByhs41jWUKG51ABaQjTaZT7ALMWkHCSYODYUxXAkiZObK/EfdLDlNVsWAAB+t+OYLhg1Q59okGC2KbKfCSwLALjcdCM1kczBaQjgjFgoJEQuH6mugix4+3rGkMqoqPQ60dg4D7jmL2RzMQN3SDRjiJihwqqsX7Hepgd3dZkagjCGqLGO3j9m1xzrIfLVZv3KYbfh0lPm4f4PbMGTnzzXdHN5K5XN/XFnF7YfHsRINIkKtwOr2TXpreISMC6bS4xpwaTbpIJHkyiHmyVE2j3JAlu7UwjqzZAvIfLVaFXwYcMQyD7SK4W65ajwONFSTTYpUaLxzMHccjmGC4VmXIdN0RLwHAyR002CF6vAdyiSRDSZ1q73TJJUidl7dvlxzrI6NAY9GBxP8DkajCFyisEjZbuJZE4foLOiQbXfpTXGv/wb8163XOBrlYFxzWmqQI7VQRnkzuGozqggHEtiJJoUEiLo12X6/woqFzWz7SYJESui/BkxOAAAYV9JREFU0PceG9GUCZXz9ceve09CsCusE4z9YnIkI0ajgiSqSJc5u6s4hogZKixvrCDOcFs+gqpQCPX0GFlgpusfYmCyOeY0x9ihypbczHmReNuGVnztLatJQc4A1uvyx52dlkOo2Xrg8Zis44UioSkCWAGEzR773Q7Sx3zeinqEfC7TP8+Cy4QhSmtMrNW1waVudnpNG0wVAkJCFAj4td8J+13S8PxmDBEAXHlaC67c0IIvX3ZStlxOhNOrV9SIYJK52AhfzwJ+8jpsze4Jx5BIZeCwKfDU0aTKuN4CAkNkNFUQe4jIfWhzB7BlEdmvnmDMuwA2c3JJAzE7sUTzWv33nkqsp8VxxpaftbTO9NoEALRSp9mcCRG9VxU72hvJc4sJkaqquI8ykG86tdn6eWYoZEJUJqiqynX2NVUh/S8LSYjEoDKd4i5T/AYTQSVg8Ndx+1DW/7LDMKCVTXlurQ3yZu9sRyNNMjcWT/HKmwjGFswrNCFiCUnVAm1jFhki9vdOH9a1VSPgdmB5YwXaanxZQWBTDQlOjwp9ROmMyu2Rt5glRAO0gVZIiBx2jQViwbZp/xCg2W4nx/W0Nz9+TdvO1mO/y47vvn0tX8Q2LqzGiqYgYskMfvWcSVVJQH9YnN9gJpmjbjyGhMioxRaPLS9D1HCSlnA7/aQfCESOIlL2iqIArRt11rP6g8ghmevWO8yJ2NRejdqAC0ORJE9uRTBWcj7rYTJjJTlDlJ0QFYKNC6vRVuPDWDyFz/2ByPs2ttdoybGiaBtqmCZE7H3anObnOEg2BjttBBfNBXppYBvwM6bFylSBzZawkEwoimCsYOgj6tUSIgBY0ciqxmRNIf1DzFAh99q0dUkdvE4SALdU+6A48zNELnpNGu3tGViwEfALCWo6ISQepDhx5WkkWb2bDgvtogyRm13zqbggmcvuIWKV5mq/i8hD61aQdWfn/+V8z1lIasUbHZjtdmIs+9pk58LthcOmIJlWdew7K8j4vd6svyGvSRMiqjawZogMfYesWuzwakWCRDjrb3X9QzYtLCiEIZqoZM5Jk+lCEyIm+2JSXgYu7e02JESCjIqzQMxYp9j+oRLggpWNcDtsONg/zoNcI5hE3csYk4kMZk1qBTDGEO3vDSOaSON+yk5xh9pCwOVeomSOmSo4MBZPmVqnD1CGSHEYzFLodR0MaPdP0O/XfqdjiBz82huJJjFMhzO3GBIin8uBW968mo8HmBDE2Gq8lx4XS4jI58J6mJqrvLBVUZfP4RwMEYvjTCVzWuJ65lJybz5uMgcvr1yOwSgDdFegyu/ihU3A4C5nBBu90fGs9b0sxGlLecFUK6a80jmKfb1jcDlseL1Vv/IMhkyIyoTB8QRGYykoClA3kYSIMUTRYf1NZBYQ1S0FLvoa8Mbv8h+tayU392Ov6m8wxqq01fi5hpuxGxx04bLTYLvXUI1UVZUnDgua6rW/MWv+Z+CW1wvN+3EEhqiuwo2HPrYV97xvEwm+3fpKfrMJQ/RK5wiv5p8ynwbkbDFSM1qvgEEewQa07qc39W666a4wLj4uv+YUM2oi6aILhd2lyRS+fPlJxHqZQlEUXEub4X/21JGcNsT9Y+KEbzOGiGwSnFmg4AmRGFjzHqI8DJHNDrSdwR+7lEoKOwajeP4wWeAtmzp1B2GwjGeIj2mJsUlC5LDbuL7ZKJuLJFI8aGxtpIGn0ESvPZAxRAXcYyZQFIWzREyiw/uHGJhEdYz2EYnsrVnT9Zp3AGveiWOL3gFAL5fqoQYLFSwgMGOIVDU/QwQA1QvIV7GPKJXQquL1NCEyyGhe6w1jKJKE12nH6vkmDLQAr8uOs5eR899W49MSwBwJkddL3psVQ8QC4toq4dpKxbJ6Wt62oQU2hejh9/WEeZCiKwKogqlC1JwhqvG7yee04b3kF9vvNGd9rWDFuLqDXMKaxRKxHiKHB00hxoxo5+OlDnINza8W12XhnNJ7OBQk54glgyKGI8JMIMYQMcttX7W2jpoxRNxhTv+eGEMUTaYxblIYG9VJ5oqz3XbToL9/LI6jAxF0DEZwoG8Mr3aPZr2WqqrcGMCYEImFm2gize9bfUJEC2Fs/eH9Q0sLO+YSIOB24LwVZL+0MlfoHiHnhsvJJpIQCYH2/CovKtwOJNMqfvKvQ+gfi6PG78JZy3JYbRuRo4dIoUVVs4SZSVQVdk9wyRz5GhISolAFfQ1DHJGEnTNNbP2oDbisWY7JwO7Q9njaIxqkx8WKNkdFUwfmVMdaAUQUZLvNYp4Ati4hn8cLHcNZhZy8hgoMDau09QfgseKGheQYnHYF562wYMcAsie7Kki8yXp9jWDXltOLpYYeYwC4jzq0XrCiAUHRBXWWQCZEZQKTy82r9MLlMWycJnKeLIh9GOzmcgV4/0sWNt0ILLmAf8ukLb/dcYxLTABNMreg1gcYrS4ZDBpfYx/RsaEoRmMpOO0K2ucJ7yUXS8Tcr6rbecUc4W5tkUzoq65NlV5U+enNLSaBNifa6ip07wXQ+oc2LRKq+U4PHzCKzhe01xewmFtvk5taY4hMFh/GEoVNNjPWt+Hw4IfvXIdf33A6Ll+bXYV745p5qPI5cXw4yh3xzFCoZM7udOti8KyNVBUmjOdjiABgy0eA9nOAVZejJqANpmUONXmrVIC1y1zPLgAqkR8GzDdkJpv72yvdOvca1o9X7XehqlII2o3X3DgzVZgYQwSQ6qmoutiy2PBcWQxRDkMFAKhsBi77PjwtRNIgBrMsAOIJkRlDlIwIkrwcCZHZLKLBgyR4cQX4fWdMiJ4+wOYPWfcPibh2y0IEPQ684eQmLXg3m+lD1xUflZH2huOmlWSWIDaErBgibU1gG/ovnz3Kz6PXJxQB2BwiNdt2WyeZA4DVbyPnpf81rRG5EFj1ECmK1kfEmEoGbiTg5rp69r4P9o3hS3/aDYBIWkzXZfr3VVR+XXAPkdjLwI5XdAvj70nvMMfgdzvgo45UZkGv7jWLlMw5XB5U0MB26zcew5lffwzn/c8/cNG3n8Cl330SkYQWGHePxjAaS8FuU7KkrqK0d1fnCNIZFfUVbjRVCsUiLpmjCRFTDJTIUKFQvPEUTTZnNtuJSSG5nGwSLnNw+qAoCpZTtcNtj5HCyBvXzCvoPufIMYeogjLbZn1E/ZSRtbHr2WC7HQpqnyNPiASGiLjQKdxUgcnljOxQScFGddD9s8qQELFRKgtq/Hy4q2lClGW7bW2qAKcPLdU+LKz1I51R8a/9elOh3XQgdd691+HWS9Hp53Yu7Rk6f0UDKr05khSbHWg5jfzfSjYnqE1YIeLoYATRRBqpdIYPJr587eyTywEyISob2Bya9jp/diBalGRuFIgY6NcCsHlRLa47gwRIn/jNThwZGMdwJMEp59ZqH7eIztL/04WLyXiMTnNc01pfAbfHDzacMGdCxBiiqoVE2mdzAFC1PgxhEc+CuEnbXVhAGyqPCAzRk/sM/UMMbEFiC5FFQrS/dwwjUc3W1TQhEvuIjEhqlZOWap9lY6fHacc7NpKF9Cf/PGz6GMAwvyGHqYLN4SYVbwqfMSFKJ7VAxezcGtG6kfQGNZB5Ncsayflh/Rim58UIUTInVt67XyZfG1db/un6tio0Bj0Ix1P49XMd3IKb9awtrgvQIJxec0ZpUo4eokLRWOnhrmZ1FW4+r4ojF0OUA/NCrH9Eu5/YvVUpVkiNYJVZxZb7M+SN40JCxPuHlnH2im2s+3rGkExn8HSB/UMMpy2sxkufvxBXrG8RGCLrHiK3xws/DarN5FEs2Giu8mn9WamYwFpoSeBV9N757fPHuAW33yewa4L7lbGHaMCYEHmCJCkCirPg5smDyWfBEv1xg7ECC2wdHsyvIn/HjABu+PnzCMdTWN9WhQ+ft8SCIaIsWigEAKYznYYjYg8RvecjYkKUgyESDCyMYEURs6B3ZBIMkeLw4FLaV+OwKfA67Qh6HHDZiazs6w/u5X/CWJ+FtX64HXrLYFap3ts9hhepocKalpB+gCVTBowcI8k7Y4imUDIHkCHUFW4HOkdieN4gZwe09aBC7KkpFoZCwnIqkQ1T1q0ouRxg3kNE77MgT4iyj5MxRA6nYTYe/VoXIp/b/CovnG6B5aXxh0rZJyZ1ZQlRWzkTIkNLQlWl3tWRtRy01fg06+5Iv34fSsa0eKAA2232OW1dwvqINHZ5aDyBTnqvryhEnSHK5uhrnru8Ab99/2Z8/S3W+y4Hk81ZJkTaMdcG3Kjxu6CqJH7654EB9IXjqPI5sTXXsN8ZDJkQlQkHqOV2e61ZQlSAyxy7kaACI7QCkSfgMuKTr1+OdW1VCMdTeP8vdnCtZ32FGz6XIwdDRL4PsMquwVjhFVqxWDUvSIIsVnm0mm8BCJK5dqJR59bbnfq/NZN1OdykPwMAHC60VZPHHBmMIJNREU2ksZ1Kus5YYpEQASSgZFUdCuY0t693jFfMm0Ne80oKk/qZOc2x4KUAFuadm9pgtyl45tAgP5ciVFVF/1gCKbGHyCjpYZuL3cUroXUVbu5kxj9TcVZPPsmcCZYK+mOnPbs6awp2nappfVWRyRabrBdmm03hM4k++/tXsPErj+Bzv9+Fh/eQAHNRvV9/zRlnEU2yh4jhvWe0w6YQ96KsyeBGhihffw/FPCqVGhxPcKaEBUChAEtkTSrColzOYg4KAK2HaMgsIVrOfzS/youA24FEOoMDfWNC/1AB65IRBTBEitPDq7pmTnPHaZLUHPIKyUBCSDy063brkjrMr/JiNJbigQJbp5BKcCbNbDDrIA3QagKCrITJ5vb8CTj0BLDzV8DfbgZ+dhnwkzeYO8bxoMDkXmDGCsa/M2GIOgYj+I97d2Jf7xgagm784J2nkn5DsyGVtKhRV0Xurb6xeJbxiN5Ugb53Vqn2CeY0Zj1E3MAi+z0x2ZwZQzQaSyKtTmwwKxwefOXyk3HwKxdj/1cuxp4vXYSXPn8hfnQ1Md356VOH8dxhkqzrDBUMYAWL/rE4HqWDr3WGCgBZD5jpyMA+7R6ZYoZIP4Q6W2nAmL/KySREhuKiWMRa1lBB9u1iwOcQ5UqIzCRz1KnN6db9DWPB2xur8IU3rsKtV5yiSb1SgqkCNWNgyZbZDKKSw6tfA2uCmisnYJDMeULami86zXF3VUWT4LFitm4wqz7mOZPK5kRjBRaTtFb7CpOgNQsJkaAmWNdWpR/kbYXWzeTrkafMZcSGfmQWP73WE8Z91LDj0lPm5TZ/mMGYnUc9C6AxRAGNiQEAxV4Y0+Nwa383RF1MeJJUGJx2G773jrWo9ruwu2uU20CzYW2mlUiAL0iVNEizYoj4wpprFglAbiwumaNVbN5HRJ3mciVEYh+R3YV5IQ8cNgWJVAbdozFsPzKIRDqDpkoPSUBFiAlRZUuWK9vCWj9sCnFhYdO7LVmQXNbbAkOUD02VXt78+ctnsun20WgKiXQGCQiVUGOwwRMiJxppQjQv5NUcfVhgzT4Tm0P7XREQGzKX1FcUttA5fVoCK8rmul8iXxtPzvnnH71gKa7a2IpKrxP9Y3H89KkjPHjggwQZU5LFEFG5wSQYIoAk1s995nx86vXLs385QYao0uvk8iPuWkTvrepKep7NJHMFJlyaZO6INlvMJCGy2RTeh/H7Fzt5/9DJzaHcz28GnoBbM0SwuzVWxKSPiFVf54W82v1p0kPEjv3tp+mLGhViMskkcya220wyVyW6azWsIkGAmgZ+eglw3/XAv74LHHwMOPIk8Oqfs9+XIHXJApfMGXuIRIaIrBH3v3gcf3m5G067gh9ctQ71FR7+GH4OAN26Wh2qhNOuQFWzE5SRqDiHyNBD5K3K3UNkIZkDSM8GkB30JtMZRBJprW+pUMkcW7to4mczuIJtXVqHt61vgaoC/3HvS4gm0jkTIr/bwYNkZpOv6x8CyB5CB3LjwGPk/Dh9WmFuCnHxyWT9eGxvb5bbXLeRMc43JNMMQg8RoDcIevO65uwCTz6YJUQsRqCmA/1mPUT0fnO6jAyRxhBevXkBYaa5rE6TzCl0vxqKJJBKZ3gPUXklc/qEqK6KJUTEFZLJ9NtqaGGO9xEJCZE4g4gZlLC1W9wPk/p15PRFNXDaFRwdjHD2u2BDBQbOECk6Zr1gNJ9K9u6xbvMh34Zkm8UHL3YM46+vkALhbJXLATIhKhtYD9FCI0Pkq9a5+OQES4BYk3SRDBFAgu/vXLkGiqJVWNqYh7/TKiEiC1eogiQXPYbFjrEafIAqCwysJHNjPeR3ik1bQIzGCrkkc4B2c9vdcNhtfFE8MhDhcrkti2uzF3sxiWQ6cgFuh50sbtAqdiuNDnMMrIfIVDLHKieFLdaXUAeWl45lM0R9NPDg1qtAdh+REFQ0BsnjmkMeYWNhvVnsvBbPDgGaHAUo0FABIBuF0WkunQR66YDQHJI5gDgG/fflJ+O5z5yPn1yzAW86tRkBtwN2m6L185gl4am4ljz4J2aqIKIm4NZbrzMYh7PyhCj3+VEUhbN5rOLYQ9lXnhCZMkTWwaoOlS0k8U3HtaSdzyDSJ3YsSPo/Ouh0/YKqiVX1uOzWmiGCw82tvs2c5o5zyZxXk8wlxrVr3FAkuWL9fDhoEF0bMLCiXDKXbbvNpDc1fn1RBGd+nKxN7iBJjja8D5hPtfRmDFGu4g2TzDEpMIPIENGEKJYkSevn37iKD9Nmj9H9DV+fFdicHp44GWVzprbbvJehOjeTn0MyZ8UQjdCEU3OZszaJ0cEwmNUMn7lkBRqDHhzqH8f//G2vMIPI/B4TmWxFAVbPD2U/iMnmXvsr+VqzqPC9uITY1E4C347BqE72HU+ltT63ihwmK/kguMwBxITC77LDZbdh25oJBKvGhEhVefIbqsjFEJH3wkdBiDJuQF+gM2GIFLsTNoW83GAkgSODQjJSLpgwRDYFSKZV7O8b42sKZ6l4H5FgvW3sHwK0vSEe1pgXg2TO73bgVGqGxWRzrxRqqMBQvwJY/17gzI9Z95vngtOrsUxHn87+vaHwy2YV/np7B6LJNBbW+rOLEbMIMiEqA9JCJSGrh6gY9yvGJLGbbQIJEUCo2A+fq0kDuPOZFUOUYk2P5GIXXeb6wnH0jMahKAKTwuVLFgkRk8uJDI0xIbJqVGZgizJdRFt5QjTODRXONMrlAP2iZOgfYmAyMNbfMDGGKP8mL4IlGuJQWAYWeIT8QnJllE4IDBFbgE6ZHxJ6MAySuUIMFUwg9s8U1D/EYHSa69tLjtkd1KRdecCGyH3zrWuw/ebzsePmC0yuOSG4Y3I5m6OofruiYZkQ5X/NeUJDfSajope6zNWxhCiTyg4sC3GYA8gGyAoOQ4dIYMF6JQwW6ew8DtGewkL7h7JQCEPk8KCFMkRGp7loQgsC54cEoxfG9AFZiWB9hQcXUtnRPF0RIKEbzGppqhAwJERLzgf+swv41FHgPQ8Ab7gVWLiV/M7YCwQICZFJgsoMY4zT6wWGiJ0LAHjb+ha8w8B4Zd/DEf63EJJqo7EC6SFiBgcZch1FBIaImyqYSebMXeYAwXrb0CfCEiKXi/U6FjeYlX9uJgh6nPjqmwiTfOc/D3EnKzOGCNB6HQFgaX2FuQsZW/9Zf0TN1MrlGPxuB0+AHxf6RZg03eWwIcBdJyfPEPlcDtxz/Sb8+sbT0RAsbH/SgUst6TUiFOeqAswpUH+cRPZN34+L3Z9MMkc/f7vw+ZsxRDYn7/frGYnz3suySuYMDJHDqRUcn6LmMw1BN7yU6ed9RKJkzmi5DWgMkZoh5zGT1tZMURJMe2/+8RrZy3Yb1Tj5oCjAJd8Ezvuvwh5vBjaP6Mi/sn9nkMwxpUGcDuq+bM0EGMgZBJkQlQHHhiJIplW4HTbMq/ROPCHiDBFLiEITPqYPn7eE2+Wy6cXWPURkQWJVKtFljrFDC2v88LNNh70/s7kwgDAUVZgOzWcRMcmc9YYMQKtc0mNmxgovHB3mVZTNiyaWEBmb5i0Dfzpg03TwYj6Gy4C2ah9cdptuKCwD20gYQwcgmyFiUgq7G286tRlP/Mc5uH5ru5Zwso2ULWATHD5Y4XHyWU2n5LFk1sHoNCfK5SawYHqcdlT6hIqimWROtNwu56LMJHORfvK5FCppA3GdBMhQ0aFIAsk0SYZrQkKgZwyCCk2IAL2xwuAhElw4/VqiRGG8xifUPwTkZojSWuBr1UPE5++47Ah6HdkJkd1tKvW84ax2BNwO4qAkJhAZzXY7lswgQTfqdEbFMA3gq40MEUASO/GaCVDpW06GyOSeYud59Jj+50Jy2Bzy4sJVDbhwVQO+sG1VdgCRJZlj/Ynk50wiaxzOSmYCGWS2ou02Y9lNGSLWQ5R9jVkxREyS6GJN80W6zOUrHp2zvB5vOrUZqko+P7/LzvuvjBAZIssKNVMIsOOc4v4hESzwffw1rV+EfZ6NQQ8UvjdPvocIIIzZhCv3RoZISHyr6B7VZ2CIxhNpHiS73WLBQtX620SLaLvwflniZHfwZPyl48NIZ1S4HDY+iLcsMM55dLjRRK85lhCxHmYA5k5zRsttgMRIbO5jbFRfPDb0SJLX6sdYPMXnIxbMEJUCuQa0MjdO2iu5tF6/Xly2duolqKVEGczcJUS5nM2mAIqw8BdiqMDAquys+jBBhggA7DYFd169Ad2jMW1TsXSZo5VU6rDSMxqDqqpQFEXrH2oWjiWfZI5pUauEhKhYyZyBIeIyt5fI3y9vrOAbtw5FMEQACcwsK1A8SOohC7sYyKT0QUs+OOw2tNf58Wp3GK/1hHW6aJYQ1VV4gF4HZQ0sJHN2FxRF0f7eWF22mplSBL79tjV4rSeM9QsmcO3yhCi/w1xRcJlcc2M0UZ1k/1Be+GrI5pZJkYC5wB4iQHSai/IAqDbgglO8btJx/XVUTELEjRUOa8dTtzRLGrS8sQIKlaNMuH8IyMMQCawIk8wN6h8nyuUURclOiEwkXAAJ8F763OvI+rqbSjHTCZ4QMelYOJZETcCNoUiCK1V0PURWYOYIuYofZsUbVugZMSZEWnJosyn433ett35tY6GKnVu6XmsMkXYuY0kSgNqzEiJBvsOlOma228W7zDGGyOtyAUkU7TKXiyFi+K9LVuKJff3oC8extLEiq9+IQZxNlGWowGCYQTddDBFAAt+vP7gXTx3oRyKVgcth44xfY6VHSxZEyVwqDvz9FmD+emD5G6yfPJekcyLgCRG9boS9qLqCMUT6a4MZKvhcdjhdHu3vxGKP2M/rEN4ve36bEzVe8vMdR4YBEHbI6hooCQySOdKz7MXzR4bwNDWfaa0R4gPTHiITyZyikIJZdJAU0GzsPlV0hYFV84Ko9rswOJ7Ar6jLapXPyVmqKUHrRnJcA/vJnspkwJ0vALt/T363npjRVPqcaAi60TMax7q2qvLKGacAkiEqA9jMFFZZh6JoycdEJHNsESnSVMEIu03RV9jMGKJMhi9INVTGE0mkMUYtO00p3HwJEWeIhISESUsKlcwx2p4G/KwPKpIgm3CW3TZDQQyRtpkuy7Hp8obpdEJwkqEokiECNP3tvl59gMIqsXUVbr0NsQhBMqcDN1UwMEQTlMwBwCktIWKxXAzEOVoA0EUZohwOc0XB7JpjFS2DPKzksNm0a2Gsu6iEiA3l7ByJcolMQ9CjN1ExzrDhDFQhCZEwi8iifwggMhpmrjLh/iEgTw+R1jfDZGIj0aRu8CC33Gbrkt2QEOUI6vh9yvsP4rz673KS+4Bp/oeoXK7S6yxsBktOhojer2ZFhspm7fhF9rJAVkT3GAuGiMmexB4ilpwwq2IAJCHikrmJDWYF8vcQedwu7fUKQRHnIuRz4RtvWY0KtwOXrLauPrfXBuCin+tay4TIsP7XTq3ltoiVTUHU+F0YT6TxArXf7hEYIs1cREggHv8G8OQ3gT98KPcgYXbNlko2zOcQhUl8IAxOra0k14vRVIFJ6GoDbo0ZSSf1sYYpQxTX9RixZJydo7LK5QBtDhGDw83dQdnIEp3ttxlDZJYQAcKeOKqX3QqFVZtN4bHMnU+QuGnlvODUytC8VUD9SvJ/kSV65Ivk6+q38rEcgNav95Z1Rdq5z0DIhKgMOMQst+uEzYVVUoupXhsToEkwRKYw6yESqj9+r5cPzmPN37tEy20GVq23lMwZHOYAvYV1Jl2AZI4xRGQRNVYitpj1DwHCoqRY9q4sqteeK2efjNOj2WgyNoJhAokHk+qJk54BrdpWG3CZzy8AspyaOMR+CkCoZpd5IzHCOIuIM0S5HeYKhplkbv9D5KswoLhsEK238w1mFdAsMEQsAGoIUrkWC9hYAYGBM0QFyCZEyVwfZU4sEsTVVAJpKjUtFFbGLICOIfK7HVyqxvqIUukMHt5NzAfmcdaaJUQ0kC/EKYlX0zWGyOMiCRHrI2KOV1mGClbIJY/NVbzxhLTizajQR1QEK5J1D2cxRORrt0lC5PcIz29kiPg8mcIHswJAncAQia5oTDLnZQkRGx6cD8WcC5DZPTs/9zq894yFlo9xOWz4+ltW4z8vXs7n7mTBX6u/h6Z4BpEIm03hIyJYH1GXjiEyXAPdu4Anv0X+HxnIXiMY4mHtvp+3pjQHK14TyXEtRlBsqKWmCqOxlG6QNmOIagIu/f0pSr91PUTiPUwTLpuDz9hjqpuyJ0RGhsjmzJJp6hiiUBv5OtatXdf8ngvpn4vv5SM5ZbdMTsnGCqyaV+K4rxC0biJfWUJ06HHgwKPEge7sT+se+oU3rsJ3rlyDtxVbNJ2BkAlRGXDD1kX44TtPxaWnCBUtFsBNhCHi35f4xjALZkRK2+5CfZAsSL3hGEZjSe6Ko7tJWaXUOBOGwUwyF2ggFuSZFK2u0I02n2SOLpwt1V5eWHHaFWy0GITKpS+hVssN2Ody8EUvr1ZXlM2J4KYKhSdES9kMpB59gKKrrrGEji2yDIJkTgdLydxUJ0Qh8jU2QkxB4iPkWE3YigmBS+bo+wv3aHOOFp9fmtfIBdF6uxiGSHCZ6xYTIkBIZqwSookyRCtMH/rJi5bjMxevwLVbFuR/XitYyW6BLCagpUqTzSVSGXzonhfwyKu9cNgUzf3KKJkrRPYjMt00mGKsxWiUfD9oHMqaD2zdiI9ms1+5eogUxVw2VxKGiJy/RoNTIaBVryu8whqXimvXpm4OUY6EyOQaY1X6eCrDh3sCgmSOM0TF9hAV3gtSiEzqsrXNuH5rtpMoh1h0qGgq7H4qI7Ya5s7o1gO2rmeSJIn4wwf1DNyx58yf9PjzJDGtbNXMXyYLp5fs1QBZi3jC4qSMK/lsRGMFrQDhFhKipCYBtDn0Ml5ThsiF2gr9/Vr+hEhgdWxOwGbjfZ8MC8RirK9a21vZ/c5ttw0MEUvG46M5FSVGc6iCLbdLibbN5OuRf5GC5sNfIN+vu0Zf2AYpZm1b01xeKeMUQSZEZUBLtQ8XndSkr1SxTW4ipgoMpXbOMmOIdBUcFw/Wekfj2EPlcvMqPfrAItccouiQFsyLN5LNri3YA/u1n+eVzJHXdTvsfKE6tbWKDJo1w/zTgNM/CFx0i/nvKd6xsRWL6vy4YEVDzsfxynFWQlT4HCIGJpkzOs3pJHM8sRjW/3HKIiGyMlWY8oRIcJljcrn6FROahWQKnoTT93fgEfK16RQtaS0nOEPUXZypAk28I4k0ZwYbaNGhNAkRrVhGh4QZROYM0byQF+/b2g6P0276+4JQEENErsn5NJg50DeGm+5+Hg/s6obLbsMP37kOp7GCBrue8/QQ6cCr6ZpkjjFEowaGqOCEyFOpHYvoNJfJ5B7MClgkRBNgiIw9RE59D1FvOMbXDZacVPpc2gywyAB4ockTym27nUMy53XZuWubKJtjckSvm75ewaYKxTFEJQVLiKaRHWJgge/Lx0cwOJ5AD2OIRMkcAPzzO6R/w10JnPQW8rOOZ82ftIMmSi0bSneg4hzA+JgWI9gcUBSFsziibG5AVDnYBcmcVSGPXQtqWphf5kStX3+NlF8yJxRW6TExmTNDm8gQKUq2bC6vZG4kp1NlQ9Cjc1OcUkMFBmas0P0S8PJvgOPbSQyx9RNTfyxTiGlJiH7/+9+jvb0dDocDGzduxJ49hOLdtWsXNmzYgKqqKnziE5/IGlo2q8ESoeC83I8TUW6GiCdEhsZNgFSEbHaeEPWMxgRPfMNx5JLMMblcoCF7s2Xnov817XhsFsGZQTIHAAtqyeta9g8BZDG+8L+B5RdbPwbAB85ZjEc+fjbq8zUv8jkjht6CCUjmrJzmNMncRBgioZ8C0BiU6ZTMFTiQtSgYnQ33Ubnc4imQywGCBXtxDJHHaedB+QtHhwFAa5jlkjnDQLxiEiJ3hcZuZFKEwWEbdjlQFENErsHvPLwPD+/phdthw+3vXofzVwpFCLYmFcUQCRVoalnOWAsmmRuk1esao+W2FRRF6BMTZHOpKPKy2dx6u1QMUVT387oKNxQ6G4UlesMRrUeK92ywRM5VQc4RC2rTcWS5VuaQzLHXBPRB7whlpbxMpleGHqKSg/U+lHItmiDqaeCrqmTuDHeZq3Tr5WR/p8W8C78MrLiU/P+YRULEmCM2R6tU4OyGwBDRRIexOKKxQr94v4lsl1UhT/yeJQs2RzZDVFPmfcwd1O4fekyiZC7ocSBkNGXhxgqGhMgYv4kMUS6WGVqy7HLYsofNTwUqmwnLqGaAP32U/GzjjVoh8ATFlCdEBw4cwLXXXotbbrkFx48fR1tbG6677jrE43FceumlWLduHbZv347du3fjrrvumurDKx8u/jphKdq2FP43RoZokqYKWWAbkhjMGAJtZnHZMxo37x8ChGq9WUJkYqjAwBMiOislF4ux4AySHCw6l//oQ+cuwWVr5uGdm9qs/67U4L0FQkKkqllV3ELAnOYArY9InN9QW+HWPnNGwzOk9dV3Dl4tpwHPdDFEou027x86pXTPL84hSqeIvhkAlryudK+RC+w6GOnQArwCeogA8CZdJnlqyEqIJsEQAXppau0S6yJDKVAQQ0SuSeY0l0hn4HXa8ZNrNuDsZQY2L0syV0gPkZlkjvxMk8yRYymYIQK04od4r4vsitU9ZWa9PRmGiN/D5Fw77Tbe18P6iBhDFPIJCRF3XaRFFdEEwiiby+EyB2h9RKK9clbfUhklcyXDadcDb/gmGcY7AyDabzOTlcZKrz5ByCTJXKy17wJaaKLT80p2L5iqaglRKRkiQJBbhgWGiDCDRhfCv7zchfteIP1zDUGPxlimEzkYIiE5ZteiYKrAIM7wKgsURStC0mOs9Drho3OHTF3UWMGJuQGb2W4DelOFXE6VAJ+zdtqCavPh4FOBNsoSJcbIsW/58PQcxxRiys/0nj178JWvfAVvfetb0dDQgPe///3Yvn07HnjgAYyMjOCb3/wmFi1ahK985Su48847p/rwyod5a4FN7y9uMnbZGSITlzlBvwuAMyY94Zj1kDAumTNJiMz6hxhYJZUlRLmqwc2nAv9xCFh/Lf/RpvYafPvKtagqJsiZLMzcp9IJraG4SDc3o9PcSDTJZ9MQU4UQeWAWQ2QyywHQ25cCGoMyXZK52HDpHeYAvanC8efJ63hCxJJ2KsDknuzaBQozPYDWFM9QzyVzNCEaOqwPLouQ5JHnEe61evP+oZIhJ0OkmSoAwDJ6rftddvz0Padhsxmzy9YkFlQUxRAluGzL59EzRJpkroggnDNEgjxWtLG3WstL3UPE/1a7brReNHLeuWTO69QSYJbIscDMIVTrjcE0S7otGCLOAoSzEyJ2ros3VZgGhshTCWx4LzFYmAFgfUR/e6UbiXQGikKLkDabltg6vMCl3yHBenAeEJxPznXnDv2TDRwgts4OD9BQYgZMnEXEGSJ9QnR4IIJ//81O3HT3DoxEk1g9vxKXr20WnE+FHiJjMmx3AAq9n1iybnOiRkiI6iuEgajlBDNWoOuKoihc6mzKUIUsGKKshEhkiJhTpfm+vH5BNe7/wBZ8+8o1E3kHpQGTzQHAln/Lfj8nIKZ8DtEll1yi+37v3r1YvHgxdu7ciU2bNsHnIxfI6tWrsXv3bsvnicfjiMcFPfPoqOVjZy3EBEixW2vWJwoWvOt6iPTMA+tvODYY4UH7Sc1WkjkTbfrgYfK12iwhMkjm8gU/M2ECslkPkRgMFmGqAGQ7zbEqW9DjgNth1xahrB4i+jnlM1WYgB14ScASuaEjdOijAjSsKt3zi0k4c5dbdG552RARIkMEUKlFYa89r1IfCHLJXLCZfJ7pBAmmWT9QjoZ3U4jFh3JbkPOiSn6GaF1bFb73jrVY0RTEojqLtcxuCJSK6SFKxfn9QCRzKu9zGSzWZQ4Q5LGCZC6P1AWAZr1t2kNUwOsbz6nBdhsgxgo7j41wp0LOEHmdWgDKHPJE5yxXgATN4lqtqvklcwUxRIVK5qYxIZphWL+gCm6HjZtV1Pjdmi28y08Y9nP+U6+waNkAvHKM9BEt3Kr9nMnomtYUdp0VA7GHiM8JopI5em3c9vcDAMg2fdPZi/Bv5y8l70U0VbCSzAHkPk5FdQyReL+2lVsux8AZIm0taqr0YH/vGB8Ir4PYQ5TJ5DdViI0ITpXW69uEB+mWCu1nk8840EDkcnMA02qqkEgkcOutt+Kmm27C6OgoFi7UNnJFUWC32zE0NGT6t1/96ldRWVnJ/7W0zH7LvyyIEjlPZekTArNgxkBpMznPy8dH+JCwJkNAp82EMakSFyKZY5XMqQ7aJwK/CUPE3rdiL9o0wOg01ysaKgA5JHP5GKLptt0Oka8JWnmubi+tqxOXzEWAfX8j/58Ku20Go4NToewNBItpEIdEPijUZtes4UXZHJfMFVgQEe3lS+XqZwUrQxVVzWJFFEXBJavnWSdDQHbluBiXuUySB+U+GqQzU4WiXeYA7V4XJXN5pC4ABIbouDYvZkIMkdFUQbuHGw2ziJjLXNBrIpkTAzMzpznR6tjiGuOyqLDmJMbOrZ8525VhMOuJDo/Tjo3tmtFSY6VwTi78KpH2bbpJ/0fzqRzu2Hb9z5nRQqnlcoCeIWJziHhCpN1TzSEv/u99m/CJC5driZ29AMkcoO1dQg+Rx2nnoz9aym2owMCMFYTr86yldXDZbThjcV324ytZQtRB3FRZj6Gl7Xb+HqIZgeqFwPV/B657eGYfZwkxrQnRzTffjEAggOuvvx4OhwNut36B9Hg8iETMZ9t8+tOfxsjICP/X0dFh+rhZDVEyV2q5HJDbZY4uYg0V5DHMBG3VvMrsIWG5BrMWIpljKNVk7XLCTDInsjBFJq1Gpzmd5TYweVMFLpmb+GDWCcF4vZZSLgdo19zQ4am122bw1wMQPusi7s8mISGqr/Do7UrN+oiK7SES2dhyJ0RmawhAGE3mOlZEspidEBUxhwjgiRljLVgPUdEuc4D5vZ5rKCsDW9dSUW2eUjGsiBVD5BAZIv0sIq2HyCUkRJTFFp2z3CYJkSifs3hffDhrLoaoYJe5aTRVmIHYKtgsN4qmPmuvAs77L82ljYEZJhx7Vj+glSVIpTZUALT7MJEtmduyuBY1fhcuX9uMv3zkTF2CJz4OmZQwP8+CIQK0a5NJ8ui1V3aHOQbGqArFzevObMeuL1yI0xeZuAQzhijcqQ3Vdvqy1zKd7fY0jcMoFo0nF2cENssx5ZI5hoceegg//OEP8fTTT8PpdKK6uhq7du3SPSYcDsPlMt/A3G53VgJ1wsHp1SQ0pTZUAARTBSGYYRs4XZx4fwPFqmaT4MbKzjURIYNXgdySOePzzGSIAxszGaL1Zhu8s/gN3ug0x2xt2SZgabttqcUWgpNMWkjWpvjcGhOEUrs6sYoVSxymym6bwe4gfQhMllSgoQIANAs2rg2G+4sXDtj7SiW066vQhKh2KVk3XH7LYcQlgyi7VVWtIMDkYr7a4qqLxgS/GIYI4HIe1tcyGktCVVUMMclcoS5zgObWJw5nzTWUVTyeQANJSEY6AH9NcUYCRobIxNK/yTCLaFjXQ8Rc5swYInrcYhLEAlCHNzv4pjA2zqczKsJUjljho8dbtGTuBN+/C8TWpXXAn4nTbqNRfWGGptXkPmEDWmsWkaJJ7yvk9/PLzBBxyRxJGFY0BbH95vOzC6UMhZgqANr1IPQQAWSNPNQ/joVT5bbmy5bMAcTxzRT+OvLYdBzooTGsWb+NjiHKL5mTmHpMC0N08OBBXHXVVbjtttuwciWxwdywYQOefvpp/pjDhw8jHo+jutpi4OZcgKJoAfFUMUTP30W+0sFcHqcdQY+2SZpOTbaSzQwdJl89leYLRKARuir7TK+WAFozrpomWnxgQpbbDEanORZw1GUxRMP6PzQwedoTChtNOjF9kjmHS/95ltJhDsi+VqbKbluEKJsrhiESTBWyAiDRWAHQV/ILYUsAwghc/Sfgmj+Xv6dKrPKL6whLiJh8bCLPBxTYQ5QdXAUoaxGOpTAaTSFFKe7iGCLWL2jiMpfvfhKttzMZoTI+AYbIhFFh1w2zah41c5mz6iES3wcg9KhZn2vOENGCDTOrAIAAl8wVYKpgIqWc61hSH+DMUGO+sQ8AuT6a1pD/M1e54zvoQNYWINhU+oPUzSFikjltbbFMhgChhygh9L6aJMPscbyHiFzHn7hwGd535kLuvFZ2GEwV8sJm04wVmIGQ2cxItkcUYLstMT2Y8oQoGo3ikksuwWWXXYZt27ZhbGwMY2NjOPPMMzEyMoKf/exnAIBbbrkF559/Puz2KWqSnqlgzFCph7IC2S5zva8Crz0AQCHDTCkahEU6y2EOsJbMsSp31UJzKZnDpa/qz4bFwe7UZkoxSQqfEzIxWZroNNdfaA9RPlMF9pgJDIwtGcQkoVySOYapstsWERA26CJkYfUVbtipTK6+wiIhYvcOc5hz+iyr96Zo3VhaEwsriNdVSRKiCUjmFCXrPvB5mWQuiQFquR1wU6OSQsHWJl0PkfVARR3Y+x49rrG5wAQZouyCC2OIukdiUFXVYg5RDobITDKXg/VijHX/WByqqmoOcy47nA76eoVI5tJJ8B4LyRABIMnEFevJ9ZIlN7MCY4FY3xAzVCgHOwSYM0SF9svyHqKUdSEPEBgilhCRe3pdWzU+84aVkxsgXQzY/l7Mfs5kc2zmnlkB2C3abrN1ZBaoYuYQpjwh+utf/4o9e/bgjjvuQEVFBf93/Phx3H777bjxxhvR0NCAe++9F7fccstUH97MQzkZIi53oRvuU98lX5e/AajVJnmzhMjnsmOhmQ+/lWSuj8gAcjpdibK52UIfG53mJsEQAXqnuT5xwjegNycQhylamSqIG006IfQQTcPCy67ZQEPp5WziRjKVdtsixCF1RdyfDrsNDTTAbDBWhJm0dPAQqbgX2z801bA7iZkIoJfeMve9yiLNbiZiqgBkVZwrvIwhSnJDhSp/cYYnXDIXGxF68grU/rP3Lc6pAiZpuy1KLcn/o8k0hiNJg8scTVCYhM1nxhCZSOZyJJ9sPUqmSTLEerOCHiEBK0QyV+y5mCP46PlLsfNzr8OGBQUqYphxAkuEOtj8oTL0DwH664Z9zrZiE6KEtdQbyLaEL/T5S43lbwBWXQ5sKsJZjd3vrJ/VrMVBtN1m6/p07MsSlpjyHqLLLrsMqtgIKGDBggXYt28ftm/fjs2bN6OuzsTRY66BBVplkcwJDNFoF7DzV+T7LR/RPYz1Ea1sCuobwBmsJHM91DY91yyUYDPQ+QJ9nlnAEAFaoMRcnCZpbS06zam0esoZIvFzj41okj0rLTarljN5wrQyRCHytbHE7BCgP9dTabctQmSIiughAoD51T50jsT4kFaOUCtJMFJRYKx75idEALm2EmNaYQWYOEM0EdttgLDNmgEadz4Lx1PcqKSoGUQAqfLanKQiPt5H3kshPUSAfhYRS6YUYbZMLlgOZtXuYY/TjiqfE0ORJPb3jXHTG53LHH8fZqYKxUnm3A4inR6NpdA/FtfPPWIJcSEuc+LMO7M+kjkKm00h57JQMOMENqCVSefKYagA6A0B+GDWAsNH9jlnktbKBiC7h6gYRryU8NcCV9xV3N8whsiMlWXgKgJVix1mgypmDmFaXebM0NzcjG3btslkiIHRt74y9FKJlchnfkgWrNbTs6pMzN3lFCtffFblSMf1m2IvS4hySHd0DNEsWRyMDNEkTBUAvdMcm1bOXebsDm0hFWVzOStt9Ge6HqJpZIhKLZcD9AneVNpti5hgDxEA/PvrluGazQtwwcoG/S/sTm1zHTw4OxIiM3OWkknmJsoQkWNSVaBjkNwDRc0gAkhxgRc/qGwukV9eBkCYRXRcz/AU4kJpxRAZihrMae7VbnKNuB02IivKSojymCoUIJkDtCJNbzhuPghWTetdz8zA3ovdPTPmys1WVDaTYqKaAXb9lvSz2t2lN69h0M0hYi5zBSYs3FQhaa1sEH/GbbeniSGaCNiazWDGEDk92nsMd5KvUjI3ozBtLnMSBeK060mictJbSv/cbOONjwHbf0z+v/nDWQ+7ZvMCVPlcuPQUC/tFcaNORkjwlooD/fvIzxpWWh+DmBDNFvqY2/EyydzkGCLRaS6aJAklZ4gAsrjGR/XW2zm12LRarpPMTUOyueISIiFYua30z+2uIBrvTBJYdF7pn78QBCYmmQOA0xZW47SFFkWO6nZiVz94UPvcZnJCZJTeAkJCNFnJXIHv23AfeFxOuBw2JFIZHBogAVZRhgoMgToSvPCEqEDtvxlDVGjPDGvmNjJEhp6GpkoP9nSNYm836TML+eg5MCZEOskcs08WJXOF9UXVBtw40DeOvnAc43GyTmUxUmpGY4zMIIeylg7z1wO7jwNPfZ98P29N6QeyMujmEOld5vKiUMlcVg/RbE6ITBgigBQ3I/3aXj5bYp45ghnHEEkYMH8d8JYfay4mpQTblNJxEnDXLgWWXpT1sJDPhas3L7AOKJxecLc4FoD37yMVQ3dl9rwhEUGhgjxbqiW82ZpJ5ljAMrFNXnSaY6gR5T1m1ts53XrYxhLRGmCnQzJ36ruBf99LLLFLDbsTePsvgXf8St/LM5UQGaJiZu3kg9hHxEwVSvn8pYaRIUonNbv9oiVzE7DdBrIDLJuDu2MeoQlR0QwRkD2ctVDGlSWC4S6NgSl0fTAyRFwyp/971ke0lzJEXHKlS4gUfbJuZqrAhifnkSfWcWOFBB/KWul16gNXdr1aoRj7cYncYPK4/r30+zIZKgDmPUQFmyowl7lk7kIe27eSs5AhMhZ+rBIiY+FstsQ8cwQyIZrLMG5Kmz9ELCSLhaIITnN0MWNyuYaVuaUROsncLFkcSmyqAGiyOYAEGbqZB2bDWXNKD+hGEhMeP1vObTFYdO7UDmM1YhKSuZwQneZmg2SOBeqMIQp3EabA7tIkZ4VCTBoUW+H3lLEwoNhJwz+Aw/0kiZkYQ2QYzsoHs+ZhXH219JhUbTh1wQyRoYeIJxHZDBGgSeZCXvr+xITIU6nvrzPrIeKSucISoj5BMhf0Osi1WbOEPOjwP3M+h2SISgijgUK5DBUAizlEBfZtcoYombuQZ2S3pquHaCKoaNQncJYJkaGwdSLuy7MYMiGayxCDjUADsPptk38ulhz00CFx9TnkcoBBMjdbeogMQRJPiCZ+/MxpDjDI5QBz620mPchlX8oer9hkA3M5MAnJXE7oEiLW8D6DEyIWqDOGiMnlgs3FF1jEpMEVKLzPxBhM2eyooAxR5wi5PyeUEBmHsxY6UNFm09a2gQP0GEvLELFZRGxAatCMITIGZmaOoEVI5gBkmyoApDgBAAceyfkcOSVTEsWh6RT9ul4uQwVAW38SY2RYNFC8ZC6TFMyAcjBEDLOJIbLZtb5BwHpMipHplwnRjIJMiOYyxAVo4w2T26SYIQILGESGKBcqhCFys2VxYDKaEpkqAJrTHCBYbjOwxVXHELFBjzkkc6JOWTYwlx4ON1DZCkDRX8eTBU+IRMncDE6InIYAfqKGCkB2QlQoshgiG08QWJ9/jfG+KgRZDFERAxXZ+x/YT74WyxCpGTK7xWKQaZNhqC/vIRKDTaMZDzunjHkECjaKMGOIshOiR3M+hxzKWkI43JocOTi/PANZGcT1JzZCvhYqmeOmConc+1YWQzSLEiJA30dUKEM0W4rAcwSziJOUKDlsNuIqF+4C1r9ncs/FmgOZZI5bbucZDun0kCrseF/hFrvTDcYMRAaJDGCSpgqAXjLHHeYY2OLKeogyaRIsARb2pS7942eLe99sxDv+Dwh366uDk0WoDYBCejsGqdxqJs/ochjY4YnOIAL0iU0xBRIxmFLsgKJwhoihaNttQLvXeb9gEQMV2fvnCVGRDBFAEggLBrrRMMNK6yESpExZDJGZZK4wWWadwBBlaJbJX3PBGSTwHTpMmE2W1BtRrMGERG7MP41YbrO5ROWCw0PuKzWtFdoKZojovalmtGs5V+8rQ6G23jMFlQUkRG7ZQzSTMcuuOImS49oHSFA/WXcaUTIXHQZGaZW4fnn+vz33ZjJYrmnN5I5hquCr1jaH8f6S9BAxp7lEOpNfMpdvlgdniIYnfVwSedCwivwrJZwewi6MdGiD/uYiQ1RMgUQMpmhCwHqIGCZmqmC03WaDWQtJiGiSPMgkcwUmAeJ7SYyRdQawlMwxhEwlc0aGyMxUoTDJnMgQOeg8On6O3QGgZSNw5EnCElkmRJIhKim2fJiwyCbusCWFopA1KDYsJERF9hABuR3kjPfHrGaIQuaPEaXVDs/0zM+TsISUzM11KEpprDpFbXrfq+T/wWbrSomIddcAl31/9iwONrsQKPVY2uIWA9FpzpIhYhsRkx0AuU0VeEIkq1CzDsxpbqybfJ3RLnMGhmiYMkQTccacqGROXMNoQhA0DLqclKkCc5krdDAroCWETGJUaBJgE3r+RJmsYX2p8DgRcGvJT6WZ7bZRMsd7QUwGs+Z5T2xdGhhPYCiS1L8mACymsrn9OWRzkiEqLSoagW3fA+qWlv+12LUTHSRfC3aZM0mITKXeRsncLOt7ZQmRYrder0XJnJTLzTjIhEiiNBAZokINFWYzAkKzdQkYIgB8Ls3KJsNiarTd1iVEuUwVWA+RZIhmHYwV9rnCEIlB0ER7iOgcnAohWXA7bPC5JlBwYf2C0SHCpBc6hwjIfv/FJAEseeJGKorp34sskSaZy+F2JTJErLmKG3fkPt+sByudUXF8OKp/TUDrIzr0uOaCaYRkiGYveEI0QckcoCXfplLvWS6ZYwUgb8i6Z1dMlGayDHqOQiZEEqUBt92OFG6oMJshWm+nSpMQ3fyGlXj4Y1tx9jKDVbEVQ2R3mS+8dtlDNOtRtVD//UxOiESGSFUn10MkBspF9RCJkjmyrYkMUY3fBWUixiLeKm3Q6FjvxHqI+DEWkQSw98PuYYfH9F4X+4jMe4gsTBXEfg7OEOW+xpx2G6ooI5TOGHqIAKDxFPJ6iTBwbLv5k0iGaPaCXTtsHyrUFttmhzansIiEaLZJ5prXA+1nAxveZ/0YkSGS+/KMg0yIJEoDUTJXqKHCbIaYEJWIIXI5bFhcX5EduBl7iMSEyAxG221Jzc8+zFaGKDaiBT25BjJbYcI9RNmSOdFUoXoiDnMASa6YPHb4qGZmUkhCZHz/k2GILBwsRYYo5DOZQ2RkiMS1gLFdBUrmgOyRALqEyGYDFp1D/m/lNicHs85eZDFERTA47P7kPUQ5el8ZZpPtNkDu0Xf/Hjjn09aP0TFEUso+0yATIonSgEvmIkAvlcydyAwR7yEqnWTOEkbb7VSehMjYfyATotmH2ZQQiQwRk8v5aiZWAbXZtUCrqB6ibMmcaKowIYc5BiaPHTqs/ayQe8od0M8jmRRDZL62NJlJ5nS224aEyGbTegoT1F2uQMkcoO9vdNltcDsMIUQ++23JEM1esOuDJenFJCzGhMisb3m2224XAtlDNKMhEyKJ0oDd3AMHSJVYsQO1U9DoOV0wY4gmYaqQE6zKm46T18rHEEnJ3OxHtVEyN4NNFUSGaDL9QwysUlxUD5HIENEeIoEhmpDDHAPrIxqiFugOb+EGMKJsrqiEyMgQma8tOoaokMGsgBbYJsZJrw8bllrA+RYZoqDXmc1ms4SocwcZS2CE7CGaveBFGdp7VkzCwuR1XDJ3AtpuFwLRZU4yRDMOMiGSKA1YQsS047VLTuwqoDiwsdwMkbtC62OIDhcumSvBfCSJaYLLDwQate9nNENEg9tkdHL9Q/z5WEI00R6ibJe5CTnMMbB7nTFExRQYxMSwKMlcYcYoYg9RsJAeIkA7r/Exvf12AQmRyBBVek0C1uA8oG4FkRYe+kf27yVDNHth7DGbiGQufgL3EBUCKZmb0ZAJkURpwIKEkaPka/2K6TuWqYBox8sTjzIlRIoi9BENCdO+8zBEDDIhmp1gsjmbc2YHkI4SM0TsvU50DpGSbaowqYSIyWP5kNwiAhlxYO9EGCLRVMEE80Jkzan0OmGns4HyMkTicFYxQC1g/ILIEFV6LQLWXLI5yRDNXhiLMsUkRExex2ZqmV1rxn1rtvUQFQKRIZL78oyDTIgkSgPjzX0iGyoABpc5usmX095atN4ulCFikAvv7ARLiNwV1jauMwFOkx6iSUnm6HWdx/VM/zdic3+2ZG5yDBG91xlDVMxcr0kzRMP0Nc3XluWNFbh+azs+c7FQgGKBpGLXB2AMPCEKFzyUlUHPEOVLiB7TrL0ZJEM0e2EsUBQlmTM8tiCG6ASUzEnb7RmNE/CKk5gWGIPuE9lQAdAYIjZ0EShv4iFab7OAJ18PEYPsIZqdYH1EM1kuBwgMUbxEDBF9vklK5gIuBxSFxOQlkcyx4axFMUST7CHKwxApioL/vNjAxrPKvbfKPJEWe4i4w1xhwZmxh8gUbZsJYzfSAQzsJ/JpBp4QSYZo1sF4jUxEMse/L2Aw64nIEDlc5NpPxeS+PAMhGSKJ0sB4c5/IQ1kBwtgYF/BybvKi9XahpgoMkiGaneAM0Qw2VAA09iIlMkST6CGqWwpAAeqWFf434jVP++1sNgUBOpx1cqYKhrlgRSVEpWKIilhbWA+RmVwO0I9IiFOnuQLliXWFMEQuH9B2Ovn//kf0v5O227MXxnVoMgyRqcuckSGaxD07k8HOo+whmnGQCZFEaSDKSJx+INQ2fccyFVAUzX2KoZySOZEhSueRnUjJ3ImBxecDi84DNt4w3UeSG6wQEB8Dwp3k/5NhiN78Y+Bju4GaRUUcg8gQaaYCZy+rR1OlB8saJ8GyBQz3eTGBTHCCPUSsgs4YomLuYRZ8+kwMFQDBVGECkrkKLUi1TIgATTZ38O/6n0uGaPbCmDQXxRAVIJkzskYnoqkCoMlYi5HeSkwJpGROojQQk4H6FXxa/AmNQD0wSividnfhVrwTgdhD5K+lr2mxYUjJ3IkBTxB41++m+yjyg937bHCpzZldLCgGDhdxKysGdvOE6P9duQYZFZrhwERgfC/FJEQVTcTkQc1MjiEqJoEQJXNmYL1ZOslcYe+pxu+GTQEyap6EaP5p5GvPLv3PpanC7EWWqUIRCYvxsaY9REbJ3AkanrJZRHJfnnGYA1GrxJRAvLlP9P4hBtZsDRQnaZkIRJc5VmWVkjmJmQAW3GaS5Gtl89QXRBzZkjmA9NhMKhkCCNOiCO+nKLbGAVTQ5G4iPUSMDS6GfW5aQwLQti3mv+eSubGiJXN2m8L7sSx7iABN7jjSob0GIE0VZjOMLGIxpgdZPURzmCFiUuhQ6/Qeh0QWZEIkURqIQcKJ3j/EEBB6C8qddHDJ3LB0mZOYWTAG65PpH5oo7NmmCiWDzQ74arXvi3WHat1EjqmYQdXGe7iYZGrBFuDTHcCWD5v/XmeqwCRzhUsK6yrIseRkiHzVGrPW/5r2c8kQzV5MhiEyJk9mCXEWQ3SCJkQXfwO45i/AwrOm+0gkDJAJkURpMCcTIoEhKvcGr7PdppV46TInMRNgvPYn0z804WMQrvlySFfFPqJi76c33Q58/LUie6IM57TY/sRcj2cJXTxctGQOAG48qx0XrGzAlsW1uR/IWKK+vdrP8rHbEjMXk5lDlMUQmSQ7WQzRCSqZ81aRosVMHqUwR3GCXnESUw6xatpwgs8gYtBJ5qaKIZKmChIzDFkM0TQkRLrBrGVIiESnuWLdoWx2wF9T3N9MhiHKB7PBrEUMwd22phnb1jTnf2DdcuDwE0Dfq9rPJEM0e2FMiIpymSvAdtt4zZ+oDJHEjIVMiCRKA38NsP69JFjw56kcnigQg6RyOswBBtttxhBZmSrIhEhiCjEjGCJzU4WSQWSIpsIdarIMUS6IPURFziEqCrkYItlDNPvg8JBig5om3xfDEBkfa9pDVACLJCFRRsiESKJ0uOSb030EUwsdQ1TuhEhgiPKaKhg2EpkQSZQTWQnRdPQQlVkyNxmGaCLIYnnLkRAVP5i1KNQtJ18lQ3RiQFEIS8Ss4CcqmbM5zE1XJEMkMc2QPUQSEhOFrmpc5oRI7CFiQUWhpgqyh0iinLDZ9KzkdCREjjJL5sTix5QkRIaEwVHC9YVJnxJjE5LMFYz6FeTr0BEgESH/lwzR7IYom5voYFYzuRxgGK5smxujOyRmFOQVJyExUYgJUbkrnkwyp2YISwQUaKqgyGqsRPkh2s5XFtBfUmoYK9Clhs5UYTokc6XsIWKDWceKHsxaFPy1gK8GgAoM7CM/S8vBrLMaYkJUlMuc0/z/IhRFu4+l6YbENEAmRBISE4UroMnRyi1Lc3q1IGKsl3wtxFTB6ZNuNhLlB2MwvNVTkzBkvX6Ze4imWzJXSoZINFVIhPU/KzW4bG4vkE4BmRT5XjJEsxPidTLROUS5PnvGHkm5nMQ0QCZEEhIThaJoleNyS+YATTY31kO+FmKqIOVyElMBxmBMh6ECYHCZK8O2ppPHTsE9VVaGiAa1qSgQGyH/L4dkDhCMFV7V2CFAMkSzFTqGqBhTBZEhysH+sGTpRLXclpjRkAmRhMRk4J/ChIgZKzCGyFKLLWw+U3FcEhKMwZiO/iHAMIeoDMGUX5TMlSl5EFFOhkhMfthaMhUMUUpMiCRDNCshXjsTlswVkBBJhkhiGiATIgmJyWAqGSLWRzTeR74WYqowFRbBEhLTzhCV2WXOV6O9hqey9M9vRDkZIrtLSxqZQUu5ZIAiQ8Rey+Ysz2ckUX7oTBWKkcwJCU5OyRzrIZIJkcTUQ/KSEhKTQe1S8jU4BY3kjCFicyAsJXNCcCgZIompAGeIZkBCVA6XObsDuPQ7xNAkUJf/8ZNFOW23FYUkQEwuB2QP3SwVGEM0eBCIjZL/S7nc7IVroqYKwv2ZK9nhDJEMTSWmHvKqk5CYDM78ONC2GVh4Vvlfi/UQMRRiqjAdDe4Scw+hFuAogMaTpuf1mUNVOlG+YGrNO8rzvGYop+02QAJbMSEql2Qu0EAYtdgI0LOL/EzK5WYvJtpDVIjtNiAZIolphZTMSUhMBu4AsOQCfQ9DucAkcwyF2G7LoawSU4GLbwXe81eg/ZzpOwbuUHUCbGtZDFGJWRWxUKLYy5ekKIrGEnW9SL7KhGj2QuwhKiZpKdZUQfYQSUwDToCdQ0JijoBJ5hisNhabAwC12paSOYmpgCcItG6aXot3VpQoh2RuqlFuhkgMbN2B8n5urI+oayf5KhOi2YsJM0Si7XaOhMguXeYkpg8yIZKQmC0wSuasEiJF0YIOKZmTmCuwn0D9B7qkQSl9EiGuC+V2zeMMEUuIZA/RrIXYQ1QMQyQmODkZIvo7yRBJTANkQiQhMVuQxRDl2DRYcCgZIom5Ah5MnQgMkZAAOTylZ3DEwLbsCRFliFjPkmSIZi9KwRDlSog4QzQFEnQJCQNkQiQhMVtg7CHKFViw4FD2EEnMFbBg6kSQzImN5+UoaogMUbmGsjIwhohBMkSzFxOeQyRK5grYt6SpgsQ0QCZEEhKzBYX2EIm/k5I5ibmCE4khsju0Cny5E6JyrxHBZj0jJRmi2QvGECm24sxLbAVK5k4k2avErINMiCQkZgsK7SESfyclcxJzBTyYOgESIkBjUsrBqIiVfleZZhAxKIommwMkQzSbwRKiYiVthUrmJEMkMY2QabiExGxBobbbgFaFlZI5ibkCxwkkmQPI+0mMlYkhMrjMlRt1y4Hj28n/JUM0e1G1EDj5rUCotbi/sxdou22XttsS0weZEElIzBYUOpgVEBgimRBJzBGwa/5EkduUkyESE6KpkNVKhujEgKIAb76j+L8TE6JcttsOabstMX2QkjkJidkCu6Nw21Nuuy0TIok5AseJJpkro1PkVNpuA3pjBckQzT0U7DInbbclpg8yDZeQmE3wVgGJMPl/ro1lzVWAqgILzpya45KQmG6w+0E5Qep8Ze0hqjD/f7kgGaK5DTHBsedymWMMkUyIJKYeMiGSkJhN8FYCdJxHzoRo/bXkn4TEXIFkiArHVLrMAUBlC5HvJiOSIZqLKFQy568jX3015T0eCQkTyIRIQmI2QbTelsPrJCQ0LD4fOPAY0LZluo+kNGBMyokgmbPZgNqlQNeLuRkCiRMThZoqrHkHuR6XXlj+Y5KQMEAmRBISswmisYKstEpIaDjlSmD120jj94kAdn+X21RhKlzmAKBhFUmIpkKiJzGzUGgPkcsPrHl7+Y9HQsIE0ya2HhgYwMKFC3H48GH+sw996ENQFIX/W7x48XQdnoTEzITIEJ0obloSEqXCiZIMAWVmiALm/y8nzvw4cPoHgVNkwDvnUChDJCExjZiWiKq/vx+XXnqpLhkCgOeffx5//vOfsXnzZgCA3X6CaMElJEoFNovI7jqxgj8JCQk9yskQuachIapZBFz431PzWhIzC6KpglQ2SMxQTAtDdOWVV+LKK6/U/SyVSmHXrl3YunUrQqEQQqEQKioktS4hoQOTzEkdvoTEiY2p6iGaKsmcxNxFoZI5CYlpxLQkRLfffjs+8pGP6H720ksvQVVVrFmzBl6vFxdddBGOHj06HYcnITFzwSRz0pZUQuLERu0S+nVp6Z97OiRzEnMXUjInMQswLQlRe3t71s/27NmDVatW4Z577sHu3bvhdDpxww03WD5HPB7H6Oio7p+ExAkPJpmTsgMJiRMbZ3wc+PCLwElvKv1z2+xAw0mAtxqoaCr980tIiLBLyZzEzMeM6cq+6qqrcNVVV/Hvv/e976G9vR2jo6MIBoNZj//qV7+KL3zhC1N5iBIS0w/JEElIzA3YbED1wvI9/3UPA+kE4PKV7zUkJACDZE7uXRIzEzN2pHcoFEImk0FXV5fp7z/96U9jZGSE/+vo6JjiI5SQmAY0rQFCrcDS10/3kUhISMxmOL2Ap3K6j0JiLkA0VZD9rxIzFDOGIfrYxz6GTZs24a1vfSsA4LnnnoPNZkNLS4vp491uN9xueWNJzDF4Q8BHXpIOcxISEhISswM2G6DYATUte4gkZixmTEK0Zs0afOYzn0FjYyNSqRQ+9KEP4ZprroHPJ+l8CQkdZDIkISEhITGbYHcCqTTgkAmRxMzEjEmI3v3ud2PPnj3Ytm0bKioqcPnll+MrX/nKdB+WhISEhISEhITEZODwAKkY4JRFbomZCUVVVXW6D6IUGB0dRWVlJUZGRkxNGCQkJCQkJCQkJKYBz94B9O0FLv6GVDlITBmKyQ1mDEMkISEhISEhISFxAuK09033EUhI5MSMdZmTkJCQkJCQkJCQkJAoN2RCJCEhISEhISEhISExZyETIgkJCQkJCQkJCQmJOQuZEElISEhISEhISEhIzFnIhEhCQkJCQkJCQkJCYs5CJkQSEhISEhISEhISEnMWMiGSkJCQkJCQkJCQkJizkAmRhISEhISEhISEhMSchUyIJCQkJCQkJCQkJCTmLGRCJCEhISEhISEhISExZyETIgkJCQkJCQkJCQmJOQuZEElISEhISEhISEhIzFnIhEhCQkJCQkJCQkJCYs5CJkQSEhISEhISEhISEnMWMiGSkJCQkJCQkJCQkJizkAmRhISEhISEhISEhMSchUyIJCQkJCQkJCQkJCTmLGRCJCEhISEhISEhISExZ+GY7gMoFVRVBQCMjo5O85FISEhISEhISEhISEwnWE7AcoRcOGESonA4DABoaWmZ5iORkJCQkJCQkJCQkJgJCIfDqKyszPkYRS0kbZoFyGQy6OzsREVFBRRFme7DwejoKFpaWtDR0YFgMDjdhzNnIM/79EGe++mBPO/TB3nupwfyvE8f5LmfHsjzPjGoqopwOIx58+bBZsvdJXTCMEQ2mw3z58+f7sPIQjAYlBfvNECe9+mDPPfTA3nepw/y3E8P5HmfPshzPz2Q57145GOGGKSpgoSEhISEhISEhITEnIVMiCQkJCQkJCQkJCQk5ixkQlQmuN1ufO5zn4Pb7Z7uQ5lTkOd9+iDP/fRAnvfpgzz30wN53qcP8txPD+R5Lz9OGFMFCQkJCQkJCQkJCQmJYiEZIgkJCQkJCQkJCQmJOQuZEElISEhISEhISEhIzFnIhEhCQkJCQkJCQkJCYs5CJkQSEhISEhISEhISEnMWMiEqMXbt2oUNGzagqqoKn/jEJyA9K8qH3//+92hvb4fD4cDGjRuxZ88eAPIzmGpcdNFFuOuuuwDIcz+V+NSnPoVLL72Ufy/PfXnx85//HK2trQgEAjj//PNx+PBhAPK8lwsDAwNYuHAhP89A7nMtP4fSwezcW+23gDz3pYLZeRch7rWAPO+lhkyISoh4PI5LL70U69atw/bt27F7927dxStROhw4cADXXnstbrnlFhw/fhxtbW247rrr5Gcwxbj77rvx17/+FYC8/qcSu3btwg9+8AN8+9vfBiDPfblx4MABfOYzn8H999+P3bt3o62tDddcc40872VCf38/LrnkEl1gmOtcy8+hdDA791b7LSDPfalgdt5FiHstIM97WaBKlAz33XefWlVVpY6Pj6uqqqovvviiumXLlmk+qhMTf/zjH9XbbruNf//oo4+qLpdLfgZTiIGBAbWhoUFdtmyZ+pOf/ESe+ylCJpNRN2/erH72s5/lP5Pnvrz4zW9+o15xxRX8+yeeeEJtamqS571MOO+889Rvf/vbKgD10KFDqqrmvsbl51A6mJ17q/1WVeW5LxXMzjuDca9VVXneywHHNOdjJxR27tyJTZs2wefzAQBWr16N3bt3T/NRnZi45JJLdN/v3bsXixcvlp/BFOLjH/84Lr/8ckSjUQDy+p8q3HHHHXjxxRdx3XXX4U9/+hMuvPBCee7LjJUrV+LRRx/FCy+8gPb2dnz/+9/HBRdcIM97mXD77bejvb0d//Zv/8Z/lutcy8+hdDA791b7LSDPfalgdt4ZjHstIM97OSAlcyXE6OgoFi5cyL9XFAV2ux1DQ0PTeFQnPhKJBG699VbcdNNN8jOYIjz22GN45JFH8LWvfY3/TJ778mNsbAw333wzlixZgmPHjuGb3/wmtm7dKs99mbFy5Uq85S1vwamnnopQKIRnnnkGt956qzzvZUJ7e3vWz3Kda/k5lA5m516EuN8Cct0vFazOu9leC8jzXg7IhKiEcDgccLvdup95PB5EIpFpOqK5gZtvvhmBQADXX3+9/AymALFYDDfccANuu+02BINB/nN57suP3/3udxgfH8ejjz6Kz372s/jb3/6G4eFh/PjHP5bnvox4+umn8cc//hHPPPMMwuEw3v72t+Piiy+W1/wUIte5lp/D1EHcbwG57pcTVnstIM97OSATohKiuroafX19up+Fw2G4XK5pOqITHw899BB++MMf4pe//CWcTqf8DKYAX/rSl7Bhwwa84Q1v0P1cnvvy49ixY9i4cSOqq6sBkE1x9erViMVi8tyXEb/61a9w5ZVX4rTTTkMgEMCXv/xlHDx4UF7zU4hc51p+DlMD434LyHW/nLDaawF53ssB2UNUQmzYsAE/+tGP+PeHDx9GPB7nwYtEaXHw4EFcddVVuO2227By5UoA8jOYCvzyl79EX18fQqEQACASieDXv/41FixYgGQyyR8nz33p0dLSotORA8CRI0fwP//zP/jWt77FfybPfWmRSqV0UpRwOIzx8XE4HA48/fTT/OfyvJcPudZ2ue6XH2b7LSD33HLCaq999tln8Za3vEWe91Jjul0dTiQkk0m1rq5O/elPf6qqqqrecMMN6iWXXDLNR3ViIhKJqCtWrFDf9773qeFwmP9LJBLyMygzOjo61EOHDvF/b37zm9VvfOMbal9fnzz3ZcbAwIBaWVmp3nbbbWpHR4f6ne98R3W73eq+ffvkuS8j7rnnHtXr9arf/OY31bvvvls955xz1NbWVrnelBkQHLdy7a9y7y09xHNvtd9mMhl57ksM8bzn2mvleS89ZEJUYtx3332q1+tV6+vr1ZqaGnXXrl3TfUgnJO677z4VQNa/Q4cOyc9ginH11VfrrEDluS8vnnrqKXXz5s2q1+tVFy5cqN53332qqspzX05kMhn185//vNra2qo6nU517dq16vbt21VVlee9nIDBgjjXuZafQ2kBg+W51X7Lfi/PfWlgvOZFiHutqsrzXmooqipH25Yax48fx/bt27F582bU1dVN9+HMScjPYPogz/30QZ776YE871OHXOdafg7TB3nupwfyvJcOMiGSkJCQkJCQkJCQkJizkC5zEhISEhISEhISEhJzFjIhkpCQkJCQkJCQkJCYs5AJkYSEhISEhISEhITEnIVMiCQkJCQkJCQkJCQk5ixkQiQhISEhISEhISEhMWchEyIJCQmJOYjPf/7z8Pv9GB4eBkAmnSuKgsOHD0/qOa+55pqSHF+p8NJLL2Ht2rXweDxYsmQJkslkzsdfc801UBQFiqKgsrISF198MQ4cODBFRwvcddddOPvss6fs9SQkJCQkZEIkISEhMWcRiUTw4x//eLoPo6z4whe+gNNPPx1HjhzBL37xC9jt9rx/c+ONN2JoaAjPPPMMnE4nrr322ik4UgkJCQmJ6YJMiCQkJCTmKOx2O37wgx/gRB5HNzAwgNNOOw0NDQ3YuHEjbLb8257b7UYoFMLy5cvxiU98Ajt27JiCI5WQkJCQmC7IhEhCQkJijuLss89GX18fHnjgAd3PjbItJqcDgAULFuDGG29EY2MjPvnJT2Lbtm2oq6vD888/DwAYHBzE2WefjWAwiKuuugrj4+P8eR588EGcfPLJCIVCuO666xCPx/nvFixYgIcffhj/+Z//icbGRuzcubOg93Dvvfdi2bJlqK2txQc/+EHEYjEAhOVRFAX/+Mc/cO2110JRFNx4441FnZ9kMonf/e53aG9vz/t6Rrng3//+dyxYsED3uy9+8YsIhUJoa2vDE088wR/7pS99CXV1dVi8eHFW8nXrrbeiqakJwWAQb3/723XnTEJCQkKiNJAJkYSEhMQcRSAQwHve8x5873vfK+rvRkZG8NnPfhZf//rXcc0112DlypV48MEHAQB//OMfcfXVV2P79u3Yu3cvvvCFLwAADhw4gG3btuGjH/0onn/+eTz//PP4xje+oXvez372s+js7MQ999yDRYsW5T2O7du34+qrr8bXvvY1PPnkk9i+fTs+9alPAQC+9a1vYWhoCFu2bMH3v/99DA0N4Vvf+lZB7++2225DKBSC3+/Hn//8Z9x55515Xy8f/vKXv2D//v3YsWMHtmzZgs985jMAgD/84Q/41re+hd/+9rf42c9+hrvvvpv/zauvvopPfepT+NWvfoUdO3Zg//79uOuuuwp6PQkJCQmJwiETIgkJCYk5jA9+8IN46KGHsH///oL/5uqrr8aqVavQ0NCAyy+/HAsXLuRmBRs3bsS1116LpUuX4lOf+hTuvfdeAMA999yDtWvX4j3veQ8WLVqEG2+8EX/4wx90z1tZWYm77roL55xzDgKBQN7juOOOO3DVVVfhsssuw/Lly/HNb34Tt99+O1RVhdfrRSgUgsPhgM/nQygUgtfrLej9XXXVVXjxxRexdOlSvOc978GGDRvyvl4+2O123H777Whvb8c111yDjo4OAMB9992Hq666Clu3bsXmzZtx3XXX8b/xeDwAgHg8jtbWVjzzzDN43/veV9B7kJCQkJAoHDIhkpCQkJjDWLRoES666CL84Ac/sHxMJBLRfc8CdfZVxMKFC/n/W1tb0dXVBQA4fvw4duzYgVAohFAohI9//OM4evSo7m8/9KEPFXXsHR0dOjlbe3s7otEo+vv7i3oeI4LBIJcG3nHHHTzhKeb1jOfs9NNP5+fL5XLx5+zq6kJLS4vuORkWLFiAH/3oR/jUpz6FmpoavO1tb5v0e5OQkJCQyIZMiCQkJCTmOD7ykY/o2BpFUZBOp/n327dvL/i5GPMBkCSooaEBADB//ny88Y1vxIsvvogXX3wRO3fuxEMPPaT7W7/fX9Rxt7a24uDBg/z7AwcOwOfzoba2tqjnscK73/1udHd345FHHsn7evnOWTAYNH2N+vp6dHZ28u/FJPHYsWNYvXo1nn/+eRw9ehRDQ0P40pe+VJL3JiEhISGhQSZEEhISEnMc559/PpYtW8a/nz9/Pl555RUMDQ2hp6cHt956a8HP9a9//Qu/+MUvsH//fnzjG9/AZZddBgB4+9vfjieeeAL79u0DAHznO9+ZtJ31ddddh7vvvhv3338/9u7di49//OO4/vrruQHEZBEMBvGOd7wDt99+e97Xmz9/Pp577jnE43G89tpruOOOOwp6jW3btuHuu+/Gv/71LzzzzDO6v9u9ezde//rX45///CfGx8ehKAoymUxJ3puEhISEhAaZEElISEhI4MMf/jD//znnnIOLLroIJ598Mi699FJ8+ctfLvh5LrzwQtxxxx1Yt24dWltb8cUvfhEAkYL99Kc/xcc+9jGsWrUKu3btwj333DOpY16/fj1++tOf4pOf/CS2bNmCdevW4atf/eqkntOI97///bj//vvR29ub8/WuvPJKtLa2YunSpXjf+95X8HG86U1vwk033YRt27bh6quvxrZt2/jvXve61+GGG27AFVdcgaVLl0JVVW7GICEhISFROijqiTyAQkJCQkJCQkJCQkJCIgckQyQhISEhISEhISEhMWchEyIJCQkJCQkJCQkJiTkLmRBJSEhISEhISEhISMxZyIRIQkJCQkJCQkJCQmLOQiZEEhISEhISEhISEhJzFjIhkpCQkJCQkJCQkJCYs5AJkYSEhISEhISEhITEnIVMiCQkJCQkJCQkJCQk5ixkQiQhISEhISEhISEhMWchEyIJCQkJCQkJCQkJiTmL/w/rkI876eH1wAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7d5f8/f2lxMTEagvJFy5cKHXp0kUKDg6WmjZtKt1///01LiqfN2+elJSUJPn5+UmdOnWSzGaz7T4A0ooVK2r8XouKiqSbb75ZCg8Pl1q0aCHde++90rRp06TWrVtLlZWVtj+Ljz/+WEpOTpaCg4OlG264QcrJyal2/hdeeEHq06ePFBAQIPXr169acISsrlCFiwMELl64/9hjj0mRkZFSeHi4ZDAYJABScnJytdCKusycOfOS5ED5Qw4oKC4ulmbMmCHFxcVJYWFh0tixY6W9e/fazrFu3TqpR48eUkhIiNStWzdp4cKFUmxsrDRz5kzbY6ZMmSJNmTKlxjHU9v3X9PdTV6jCxeefOXOmNGzYMEmSJKmyslLq16+fFBsbKwUFBdm+x+HDh1+S+leXM2fOSDfffLMUFhYmxcbGSn//+9+lqqoq2/3z58+XmjdvLkVEREgTJkyQ1q5dW2OoQk1hIY6+/PJLCYD0zjvvXHJfdna2dNttt0mRkZFSVFSUdOONN0onTpxw+nsgInKXQZKcbG4nIqIGb+XKlRg+fDiys7MRGxtb42MMBgO++eYbXH/99aqMYdmyZbjhhhvw8ccfo1mzZpAkCUeOHMGMGTPw7LPP4qGHHlLldX3RM888gz/++AMvvvgiwsPDUVlZidWrV+Ppp5/Gzp070bVrV62HSESke2yZIyIiXenbty+uv/56PPLIIzh79iwAICkpCVOnTq222ScBt912G3bs2IFJkyYhNzcXgYGBaN++Pd566y0WQ0RETuIMERERERERNVpMmSMiIiIiokaLBRERERERETVaLIiIiIiIiKjRYkFERERERESNVoNJmbNYLDh16hTCw8Nd2riPiIiIiIgaFkmSbBtv17dxeYMpiE6dOoWkpCSth0FERERERDqRlZWFFi1a1PmYBlMQhYeHAxDfdEREhMajISIiIiIirRQUFCApKclWI9SlwRREcptcREQECyIiIiIiInJqKQ1DFYiIiIiIqNFiQURERERERI0WCyIiIiIiImq0GswaIiIiIiLyLkmSUFVVBbPZrPVQqBHy9/eHyWTy+DyaFkRPPfUU9uzZg59//hkAkJ6ejjvuuAOHDx/G3XffjVdeeYV7ChERERHpUEVFBU6fPo2SkhKth0KNlMFgQIsWLRAWFubReTQriNLT0/Hee+9h+/btAIDy8nJMmDABY8aMwaJFi/DQQw9h/vz5uOOOO7QaIhERERHVwGKx4OjRozCZTGjevDkCAgJ4EZu8SpIkZGdn48SJE2jTpo1HM0WaFESSJOG+++7DI488glatWgEAlixZgvz8fMyePRshISF48cUX8cADD9RaEJWXl6O8vNz2eUFBgVfGTkRERNTYVVRUwGKxICkpCSEhIVoPhxqpuLg4ZGZmorKy0qOCSJNQhQ8//BA7duxAamoqFi9ejMrKSuzcuRP9+/e3/afq2rUr9u7dW+s5Zs2ahcjISNtHUlKSt4ZPRERERACMRuZzkXaUmpX0+r/ioqIi/POf/0SbNm1w4sQJzJ49G0OHDkVBQQFSU1NtjzMYDDCZTMjNza3xPH//+9+Rn59v+8jKyvLWt0BERERERA2E1wui77//HsXFxVi+fDmeeeYZ/PHHH8jLy8PHH3+MwMDAao8NCgqqdaFeYGAgIiIiqn0QERERERG5wusF0YkTJ9CvXz/ExMQAAPz8/NC1a1eUlZUhOzu72mMLCwsREBDg7SESERERUQM0f/58GAyGSz5Wrlzp8bkzMzPdauFy93muqul779y5s+qv6wu8XhAlJSWhtLS02teOHTuG119/HRs2bLB9LTMzE+Xl5bbCiYiIiIjIE5MmTUJubi5Wr14NAMjNzUVubi4GDx7s8blbtmxZ61IPNZ7njs6dO9u+59zcXKxfv94rr6t3Xi+Ixo0bh3379uH999/HiRMn8NZbb2HHjh0YPXo08vPzsXDhQgDASy+9hJEjRyqy2RIRERERqUuSJJRUVGnyIUmSU2MMCAhAVFQUwsPDAQBRUVGIioqCn5/nwctGoxFRUVFee547TCaT7Xt2/HNo7Lweux0TE4PffvsNjz/+OB577DEkJCRg0aJFaN26NebOnYtJkybhySefhNlsxqpVq7w9PCIiIiJyQ2mlGR3/9bsmr73332MQEuD529qpU6ciJSUFrVu3xvPPP48ZM2bgwQcfBACsWbMGM2bMwMGDB9GpUyd88skn1VrOMjMzkZqaWq04W7lyJaZOnYq33noLM2bMQEFBAZ577jk89NBDHj1v165dmDRpEs6cOYPJkyfj119/xQMPPIAZM2a4/b2npKTgo48+wvLly/Hxxx/j999/R7du3eq979tvv8U//vEP5OTk4Oabb8Zrr72GoKCgep+nJ5pkJfbv3x/r1q1DSUkJMjIyMHHiRADAxIkTcejQIcydOxf79u1Dp06dtBgeERERETVSv//+O9577z3Mnj3b9h7VYrHg+uuvxw033ICMjAwMHDgQTz75pFPny8nJwUsvvYRffvkFzz33HJ588slLlo+4+rxp06bh1ltvxfLlyzFv3jx88sknuO222+o95+7du6vNEL3wwgvV7n/mmWdw6tQpfPnll7a9Quu6b8uWLZgyZQpefvllrF27Flu2bMFTTz3l9Dn1QpONWeuSmJiIxMRErYdBRERERC4I9jdh77/HaPbaSsnIyMDBgwcRGRlZ7es7d+5EZGQkdu3ahcLCQhw8eNCp8xUVFWHOnDno3Lkz2rZti4cffhjnzp1DcnKy28/bsWMH5s+fj7Zt26Jjx47IzMzEgAED6h1Lu3bt8Ouvv9o+v7hVLzIyEvPnz6/xuTXd9+GHH+LWW2+1FY6zZ8/GyJEj8cYbb9iCIuo6p17oriAiIiIiF5grgZPbgOY9AD8ms5J2DAaDIm1rWps8efIlxZDRaMTs2bPx0UcfIS0tDcnJyTCbzU6dLzo62tYmJqcnO7Pmqa7ntW7dGuvXr0dsbCwOHTqEjh07OjWWgIAApKSk1Hp/XS13Nd2XlZWFoUOH2j5PS0tDaWkpzp8/j7i4uHrPqRfcXpiIiMiXbf4I+Hg0sOE9rUdC1CCEhoZe8rWVK1dizpw52LdvH7Zs2YK77rrL6fO5u1dmXc/r1KkTHnroISQkJGDy5MmKrcup6Xuv676WLVsiIyPD9vmRI0cQEhKC2NhYp86pF75fxhMRETVm2QfE7flD2o6DqAErKioCAOTn5+Pw4cN47LHHnE62U1pGRgZWr16NdevWITIyEklJSU4/12w2Iy8vr9rXIiMj3d4H6e6778awYcNw5ZVXokOHDnj88cdx7733emVfJSVxhoiIiMiXleRUvyUixY0dOxZXXXUVevbsiWnTpuGee+7BqVOncPbsWa+PJSUlBU2bNsWwYcOQmpqK4OBgTJs2zannpqenIzo6utrHyZMn3R5L7969sWDBAvztb3/DoEGD0KtXL8yaNcvt82nFIGlV3iqsoKAAkZGRyM/Pd3tqkoiIyOd8ciVwbB3Qoi9w959aj4YaibKyMhw9ehSpqam2iGXyjnnz5uHrr7/GRx99hJCQEOzcuRNXXnklzp071+jeA9f179CV2oAtc0RERL5MnhkqvaDtOIjIKy6//HJ88cUX6Ny5M0pLS5GamorXX3+90RVDSmJBRERE5MvYMkfUqKSmpmLZsmVaD6NB4RoiIiIiX2WxACXWmaHSPMDiXAwwERHZsSAiIiLyVWV5gCQXQZIoioiIyCUsiIiIiHxVyUXrhriOiIjIZSyIiIiIfFXJ+Ys+5zoiIiJXsSAiIiLyVRcXQCyIiIhcxoKIiIjIV11SELFljojIVSyIiIiIfBVniIhcMnToUDz99NPVvtajRw/85z//cer5mZmZMBgMLt/nymOcpeS56rJy5UoYDIZqH2FhYaq/rjexICIiIvJVxRetIWKoAlGdRo8ejVWrVtk+z8/Px65duzBq1CiPz92yZUvk5uZ6fB5Hl112GebPn++116tNREQEcnNzbR8nT570yut6CzdmJSIi8lVyi1xAOFBRyBki0pYkAZUl2ry2fwjgxGzJ6NGj8fzzz6O0tBTBwcFYu3YtIiMj0bt3b4+HYDQaERUV5fF59Ph6BoPBq9+bt3GGiIiIyFfJBVBsG+vn3rlaTFSjyhLgxebafDhZiPXu3RthYWHYsGEDAGDNmjW4/PLLYTKZbJ93794dISEh6NOnD9LT053+9mtrYVu8eDFat26NJk2aXDLbU9vrTZs2DQaDAatWrcIdd9wBg8GAadOmOfV66enpGDx4MCIjI3HllVfixIkTAETrW0pKCn766SckJycjOjoab731ltPfX23kWazZs2cjOTkZP/30k1P3rV69Gt27d0d0dDQmTZqEvLw8p56nBhZEREREvkqO3Y5ta/2cM0REdTEajbj88suxevVqAOJN+ejRowEAFosF119/PW644QZkZGRg4MCBePLJJz16vbNnz+Kmm27C3/72N2zcuBG//vqr7b66Xu+NN95Abm4uBg0ahHfffRe5ubl444036n29oqIijB49GqNGjcKuXbuQlJSEq6++GhaLBQCQk5ODl156Cb/88guee+45PPnkkygtLa33vPn5+YiKirJ93HfffdXu/+CDD7B8+XJ8+OGHGDhwYL33ZWVl4corr8QDDzyArVu3oqioCFOnTnX6nEpjyxwREZGvuniGiGuISEv+IcDTp7R7bSeNGjUKixYtQmlpKbZu3YovvvjCdt/OnTsRGRmJXbt2obCwEAcPHvRoWL/99hvS0tJwzz33AACee+45XHnllfW+XnBwMIKDg+Hn54eQkBCn29V+/vlnhIeHY+bMmQCAt956C3Fxcdi0aRMAUTDNmTMHnTt3Rtu2bfHwww/j3LlzSE5OrvO84eHh2LFjh+3zi0MVioqKsHr1avj7+1/y3Jru++yzzzBw4EDbn8v777+PxMREnDlzBgkJCfWeU2ksiIiIiHyVvIaIM0SkBwYDEBCq9SjqNXr0aDzyyCNYvXo1WrZsiZSUFABi9mj27Nn46KOPkJaWhuTkZJjNZo9e6/Tp00hKSrJ9npaWZjtW4/WysrKQmppq+zwwMBDNmzdHVlYW4uLiEB0djW7dugEAAgICAACSJNV7XqPRaPtzqsm0adNqLVxqui8rK6van0Xz5s0RGBiIrKwsW0FU1zmVxpY5IiIiX1RVDpQXiGO5ICrNBaytMURUs5SUFLRo0QKvvvqqrV0OEGts5syZg3379mHLli246667PH6t+Ph4nDplnzU7fvy4S69nNBqdKlhkLVu2xNGjR22fl5WV4dSpU2jZsiUAkRanhtDQ2gvhmu5r2bIlMjIybJ+fPHkS5eXltnHWd06lsSAiIiLyRfLskMEExFivCEsWoCxPsyER+YpRo0Zh2bJl1eK2i4qKAIj1MuvWrcNjjz3mUjFSkzFjxuDAgQNYsGABjhw5gmeffdal12vdujWWLl2K06dPY+nSpfXOII0fPx6FhYV47rnncOzYMTz88MNo06YN+vTp49H3IUkS8vLyqn14Mpt122234a+//sKHH36Io0ePYvr06Zg4cSKaNm3q0TjdxYKIiIjIF8ntcSExgF+giN4G7IUSEdVq9OjR8PPzw+WXX2772tixY3HVVVehZ8+emDZtGu655x6cOnUKZ8+edft1EhMT8cUXX+C5557D4MGDMWjQIJde75lnnsGxY8eQmpqK6dOn28IRahMWFobff/8df/zxB7p06YLjx4/jxx9/hNHo2Vv+goICREdHV/tYv3692+dr0aIFfvnlF7z77rvo0aMHQkND8cknn3g0Rk8YJE9LX50oKChAZGQk8vPzVZsOJCIi0o2MlcDCq4G49sADG4E3uwJ5x4C7/gSS+mo9OmrgysrKcPToUaSmpiIoKEjr4VAjVde/Q1dqA84QERER+SLbDFGs9Tam+teJiMgpLIiIiIh8kdwaJxdCIU2qf52IiJzCgoiIiMgX2WaImlS/5QwREZFLWBARERH5ouLz4lYuhIKtM0XcnJW8qIEsRScfpdS/PxZEREREvkieCQqV1xBxhoi8R94ws6SkROORUGNWUVEBADCZTB6dx0+JwRAREZGXXdIyF239OmeISH0mkwlRUVE4d+4cACAkJAQGg0HjUVFjYrFYkJ2djZCQEPj5eVbSsCAiIiLyRY77EAEMVSCvS0hIAABbUUTkbUajES1btvS4GGdBRERE5Isujt0OZuw2eZfBYECzZs0QHx+PyspKrYdDjVBAQIDHm84CLIiIiIh8jyTVnjLHUAXyMpPJ5PEaDiItMVSBiIjI15QXAmaxmPjS2O0LomAiIiKnsCAiIiLyNfLskH8IEBAijuW1RJIZKMvXZlxERD6IBREREZGvkYMT5FkhAPALBALCrPdzHRERkbNYEBEREfmaEnlT1pjqX7cFK3AdERGRs1gQERER+ZqLE+ZkcoHEYAUiIqexICIiIvI1FyfMyUIYvU1E5CoWRERERL6mWG6Zu7gg4uasRESuYkFERETka2qbIeLmrERELmNBRERE5GvkGaDQWmaIuIaIiMhpLIiIiIh8DdcQEREphgURERGRrympbQ2RXBDlenc8REQ+jAURERGRr6k1drtJ9fuJiKheLIiIiIh8ibkKKM0TxwxVICLyGAsiIiIiX1KaC0ASx8HR1e9zDFWQJK8Oi4jIV7EgIiIi8iXy7E9wNGDyq36fvIbIUgWUF3h3XEREPooFERERkS+pLWEOAPyDAf8Q6+MYvU1E5AwWRERERL6ktoQ5mW0dEQsiIiJnsCAiIiLyJbUlzMnktjluzkpE5BQWRERERL7EVhDF1Hw/o7eJiFzCgoiIiMiXFNexhghw2JyVBRERkTNYEBEREfkSudAJra1lTp4hYsscEZEzWBARERH5krpS5gBuzkpE5CIWRERERL6kvoLIcXNWIiKqFwsiIiIiX1JvQcTYbSIiV7AgIiIi8iUsiIiIFMWCiIiIyFdUlACVJeK4vpY5riEiInIKCyIiIiJfIRc5Rn8gMLzmxwQ7bMwqSd4ZFxGRD2NBRERE5CscI7cNhpofI88QmSuAiiLvjIuIyIexICIiIvIV9a0fAoCAEMAvqPrjiYioViyIiIiIfIWtIIqp+3HcnJWIyGksiIiIiHyFrSCKrftxwUyaIyJyFgsiIiIiX+FMyxxgn0Hi5qxERPViQUREROQris+LW2cLIq4hIiKqFwsiIiIiX+GYMlcXriEiInIaCyIiIiJfIRc4TocqcIaIiKg+LIiIiIh8hbNriIK5hoiIyFksiIiIiHxFibNriDhDRETkLBZEREREvsBicWiZq28NUbS45RoiIqJ6sSAiIiLyBeX5gGQWx9yYlYhIMZoURDNmzIDBYLB9tG7dGgCQnp6OPn36IDo6Gk8++SQkSdJieERERPpTbG1/CwgH/ALrfmywQ+w2f5cSEdVJk4Jo69at+OWXX5Cbm4vc3Fxs374d5eXlmDBhAnr16oUtW7Zg7969mD9/vhbDIyIi0h9b5HY964cA+wyRuRyoLFFvTEREDYDXC6Kqqiqkp6dj6NChiIqKQlRUFMLDw7FkyRLk5+dj9uzZaNWqFV588UXMmzfP28MjIiLSJ2cT5gAgIBQwBVZ/HhER1cjrBdGuXbsgSRK6d++O4OBgjB07FsePH8fOnTvRv39/hISEAAC6du2KvXv31nqe8vJyFBQUVPsgIiJqsJxNmAMAg8G+zojriIiI6uT1gmjfvn3o1KkTvvzyS+zduxf+/v647777UFBQgNTUVNvjDAYDTCYTcnNzazzPrFmzEBkZaftISkry1rdARETkfbYZonoS5mSM3iYicorXC6Jbb70VGzZsQJ8+fZCamop33nkHf/zxBywWCwIDqy8SDQoKQklJzb3Pf//735Gfn2/7yMrK8sbwiYiItGEriOpJmJMFM3qbiMgZfloPICoqChaLBQkJCUhPT692X2FhIQICAmp8XmBg4CUFFBERUYNV7MIaIsfHlbIgIiKqi9dniB577DF8/fXXts83b94Mo9GILl26YMOGDbavZ2Zmory8HDExTl4JIyIiashsKXPOtsw5RG8TEVGtvD5D1L17d/zjH/9AQkICqqqqMGPGDEydOhWjR49Gfn4+Fi5ciMmTJ+Oll17CyJEjYTKZvD1EIiIi/XElZc7xcWyZIyKqk9cLosmTJ2Pfvn24+uqrER4ejmuuuQYvvvgi/Pz8MHfuXEyaNAlPPvkkzGYzVq1a5e3hERER6ZOrBVEwZ4iIiJyhyRqiWbNmYdasWZd8feLEiTh06BC2bNmCgQMHIi4uToPRERER6ZC7M0RcQ0REVCfNQxUulpiYiMTERK2HQUREpB9VFUC5db89l1vmOENERFQXr4cqEBERkYvkWR6DEQiKcu45IXLsds37+RERkcCCiIiISO+Kz4vb4BjA6OSvbs4QERE5hQURERGR3rkauQ3YQxWqSoGKmjc5JyIiFkRERET652qgAgAEhgNGf3HMYAUiolqxICIiItI7W0HkwmblBgM3ZyUicgILIiIiIr2zFUQutMwB3JyViMgJLIiIiIj0zp2WOcfHc4aIiKhWLIiIiIj0Tk6Zc7UgCrZGb5cyepuIqDYsiIiIiPTOnZQ5gDNEREROYEFERESkd/IaIFdCFRwfz4KIiKhWLIiIiIj0zuM1RAxVICKqDQsiIiIiPZMkoEReQ+Riy1wwZ4iIiOrDgoiIiEjPKooAc4U4dneGiBuzEhHVigURERGRnsmzO37BQECIa8+1rSFiQUREVBsWRERERHpW7Ob6IYAFERGRE1gQERER6Zktctudgsj6nMpioLJMuTERETUgLIiIiIj0zN2EOQAIjACMfuKY64iIiGrEgoiIiEjPbAlzbhREBgOT5oiI6sGCiIiISM9sM0QuRm7LuDkrEVGdWBARERHpmSctc47PY7ACEVGNWBARERHpmS1lLsa95wdHi1vOEBER1YgFERERkZ7ZUubcbZmTN2fNVWY8REQNDAsiIiIiPVOsZY4zRERENWFBREREpGceF0TcnJWIqC4siIiIiPTKYra3urmdMscZIiKiurAgIiIi0qvSXACSOJbDEVzFfYiIiOrEgoiIiEiv5CImKAow+bl3DluoAlvmiIhqwoKIiIhIr4rPi1t31w8BXENERFQPFkRERER65WnkNmAviCqKgKpyz8dERNTAsCAiInVUlQNfTgJWvqT1SIh8l6cJcwAQGAkYrL/uOUtERHQJFkREpI7j64EDvwBrZgPmKq1HQ+SbSuSWuRj3z2E02oMVuI6IiOgSLIiISB1n94hbczmQe1TbsRD5KnlGx93IbRmjt4mIasWCiIjUIRdEAHBur3bjIPJlSrTMAQxWICKqAwsiIlLH2XT78bl92o2DyJcpkTLn+HzOEBERXYIFEREpz1wFZB+wf84ZIiL3KJEyB9g3deUMERHRJVgQEZHyLmQAVWX2zzlDROQe2xoihWaIGKpARHQJFkREpDy5XS6ihbjNOQJUltX+eCKqmW0NkQcpc47PZ8scEdElWBARkfLkFrlWw4GgKEAyA+cPajokIp9TWQpUFotjxVLmOENERHQxFkREpDw5YS6hCxDfURyzbY7INfJsjtEfCAz37FwMVSAiqhULIiJSntwyF98RiO8gjhmsQOQax8htg8Gzc3FjViKiWvlpPQAiamDKCoC84+K4aScge7845gwRkWvkyG1PE+YAtswREdWBM0REpCy58AlvLhZys2WOyD22hDkPAxUcz1FeAJgrPT8fEVEDwoKIiJQlt8s1tRZCcstc/nExe0REznFsmfNUUCRgsP7K5ywREVE1LIiISFnyWqGmncRtSAwQliCOHTdrJaK6lVhb5pQoiIwmkfgIMFiBiOgiLIiISFlywlzTzvavMViByHW2GSIF1hAB3JyViKgWLIiISDmSBJy9aIbI8ZjriIicp2TLHMDNWYmIasGCiIiUk38CKM8HjH5Akzb2r3OGiMh1xXJBpECoAsCkOSKiWrAgIiLlyO1yse0AvwD7120FEWeIiJwmz+QoEbsNcIaIiKgWLIiISDm2hLlO1b8e117cFp+z761CRHVTumXOtjlrrjLnIyJqIFgQEZFybIEKHat/PSAUiE4Rx5wlIqqfJKmwhkhumeMMERGRIxZERKQcW+R250vvs23QynVERPUqywMkszhmqAIRkapYEBGRMirLgPOHxPHFLXMAgxWIXCEHHwSEA36BypyToQpERDViQUREyjh/QFzRDooCwptder9thogtc0T1KlE4YQ6wryHiDBERUTUsiIhIGWcd2uUMhkvvd0yakyTvjYvIF8nhI0olzAHcmJWIqBYsiIhIGbUlzMmatBH7E5UXAAUnvTcuIl+kdKCC47nK8gFzlXLnJSLycSyIiEgZtSXMyfwCgCatxTHb5ojqpkZBFBwFwDp7y+htIiIbFkREpIy6EuZkDFYgck6JtWVOyYLIaLIWReA6IiIiByyIiMhzRdlA0VkABvsmrDVhsAKRc+QkOCULIsBhc1auIyIikrEgIiLPnbO2y0WnAIFhtT+OM0REzlGjZc7xfJwhIiKyYUFERJ6zJczVEqggk2eIsg8AFrO6YyLyZcUqtMwB3JyVtFWaB1gsWo+C6BIsiIjIc7ZAhTrWDwFiBskvGKgqA3Iz1R4Vke+SCxYlY7cBbs5K2tn/K/ByMrDxfa1HQnQJFkRE5Dlb5HYtCXMyowmIayeO2TZHVDvV1hBFW8/PGSLysh2fi9tDf2g7DqIasCAiIs9YzED2fnFc3wwRwGAFovqYK4HyfHGs1hoixm6TN5mrgKNrxPH5Q9qOhagGLIiIyDMXMkQLnH+IaImrD4MViOomz94YjEBQlLLnZqgCaeHUNnuRX3ACKC/SdjxEF2FBRESekdvl4juIlrj6yDNEZ1kQEdVILlaCYwCjwr+mbaEKXENEXnRkefXPcw5rMw6iWrAgIiLPyIVNfD3rh2TyDFHOYaCqXJ0xEfkytSK3Hc/JGSLypiMrqn/OtjnSGRZEROQZZxPmZBHNgcBIQDLzlyJRTeTIbaUT5gD7xqwsiMhbyvKBE5vFcdpwcXv+oHbjIaoBCyIi8owtYa6ePYhkBoPDOiIGKxBdwjZDFKP8ueUZorJ8sdCdSG1H14gLYDGtgNYjxddYEJHOuFwQpaSkYP369WqMhYh8TXkhkHdMHDtbEAEMViCqi1qR24A9dhsSUJan/PmJLpZhbZdrdTkQ21YcszuAdMblgqhv375Yu3atGmMhIl8jz/CEN3Ptajajt4lqV2JtmQtRoWXO5AcERVpfh8EK5AVyoEKry4E4a0GUc1hs2UCkEy4XRO+99x4WL16MV155BaWlpWqMiYh8havtcjLOEBHVTs1QBcfzch0RqS03U2zNYDABKYOByCTALwgwlwN5x7UeHZGNywVRfHw81qxZg6eeegphYWEwmUwwGo0wmZyI2yWihkUOVHA2YU4mF0R5x7gfBdHF1C6I5GCFUs4QkcrkdLmkvkBQhNiaoUlr8TW2zZGO+Ln6hKNHj6oxDiLyRXLktrMJc7LQWCA0Hig+B2QfAFr0Un5sRL6qmDNE1EA4tsvJYtuI7oLzB4G2o7UZF9FFXJ4hSk5OrvXDHWPHjsX8+fMBAOnp6ejTpw+io6Px5JNPQpIkt85JRF4gSQ6R2y62zAFsmyOqjVyohKpVEHFzVvICcxVwdJU4luO2AYdgBSbNkX64Fbv9008/4corr0SXLl0wbtw4LF682K0X//zzz/H7778DAMrLyzFhwgT06tULW7Zswd69e22FEhHpUP4JoDwfMPrZf8G5Qi6iGKxAZCdJXENEDcOp7SLePSgSaN7D/nUmzZEOuVwQvf/++7jnnnswZMgQ/POf/8TAgQNx11134YMPPnDpPBcuXMDjjz+Odu3aAQCWLFmC/Px8zJ49G61atcKLL76IefPmuTo8IvIWeWYnti3gF+D68zlDRHSpimKx4BxQcQ2RNXqbM0SkJjluO3WYSDeUxbYRt5whIh1xeQ3RK6+8gm+++QZDhw61fW3w4MGYOnUq7rvvPqfP8/jjj+Oaa66xJdXt3LkT/fv3R0hICACga9eu2Lu39jdK5eXlKC8vt31eUFDg6rdCRJ5wN2FOxuhtokvJkdt+wUBAqDqvIRdaDFUgNdnWDw2v/nU5VKHkvCjK1diAmMhFLs8QlZWVITIystrXIiMjUVZW5vQ5VqxYgWXLluHll1+2fa2goACpqam2zw0GA0wmE3Jzc2s8x6xZsxAZGWn7SEpKcvE7ISKPuJswJ4sTs8MoOsMr1UQytdvlAIc1RGyZI5WUFQBZm8SxY6ACIAr9SOt7NrbNkU64XBDdfffduOmmm/DVV19h06ZNWLRoEW6++WanZ4fKyspw3333Yc6cOYiIiLB93c/PD4GBgdUeGxQUhJKSkhrP8/e//x35+fm2j6ysLFe/FSLyhLsJc7LAcCCqpThm2xyRIF8cUPOquW0NES9EkEoy1wKSGYhJA6JTLr2fbXOkMy63zD377LOIjIzEzJkzkZWVheTkZNx33314+OGHnXr+888/jz59+mDcuHHVvh4TE4P09PRqXyssLERAQM1rEwIDAy8poIjIS6rK7b/I3G2ZA8TsUt5x0TaXMliZsRH5smJry1xorHqvwVAFUpvcLpc2vOb7Y9uKx7AgIp1wuSAyGo14/PHH8fjjj7v1gl988QWys7MRFRUFACgpKcHXX3+NlJQUVFZW2h6XmZmJ8vJyxMSwt5RId7IPiKt/QVFARHP3zxPfATj4G2eIiGTeaJmTN2YtywMsZrFZJpGSatp/yJFthogtc6QPLhdEnlqzZg2qqqpsnz/xxBPo378/pk6dio4dO2LhwoWYPHkyXnrpJYwcORImE39QE+mOXMA07QQYDO6fh8EKRNV5cw2RZBGxyFzUTkrKPQZcOAIYTEDqkJofY4vePuC9cRHVweWCKCUlBV9++SUGDBjg1gu2aNGi2udhYWGIjY1FbGws5s6di0mTJuHJJ5+E2WzGqlWr3HoNIlKZpwlzMsfobUnyrLgiagjklLkQFVvmTP5AYARQXsCUL1KeHLfdoo/Yg6gmckGUmylasP24BIK05XJB1LdvX6xdu9btguhijpuvTpw4EYcOHcKWLVswcOBAxMXFKfIaRKQwOWHO04KoSRtxFbEsHyg87Vn7HVFD4I1QBfn85QXWGanW6r4WNS61xW07CmtqL8ovZNgvjhFpxOWUuffeew+LFy/GK6+8YttDSEmJiYm4+uqrWQwR6ZktctvDgsg/CGjSShxzHRGRd1rmAPs6IgYrkJIsZiDD2t1T2/ohQHQDMGmOdMTlgig+Ph5r1qzBU089hbCwMJhMJhiNRq71IWosis8DRWfFsRJX9Wxtc1xHRGRLmVO7IOLmrKSGUztEWEdgJNC8Z92Pta0jYkFE2nO5Ze7o0aNqjIOIfIU8OxSdCgSGeX6++I7A3h9ZEBEB9hkbNWO3AW7OSuqQ2+VShwCmet5iMmmOdMTlgig5OVmNcRCRr1Bq/ZDMMViBqDGzmIHSXHHsrRkibs5KSqovbtsRZ4hIR1xumSOiRu6c0gWR9Tzn9gMWizLnVIrFDJzZLRLwiNRWmgfA+m8tOFrd1+IMESmtvBA4sUkcu1QQHeLPWNKcywVRSkoK1q9fr8ZYiMgXKD1DFJMKmAKBqlIgL1OZcypl1SvA+4OB7Z9pPRJqDOTI7aAoEY2tJjlUQZ6RIvJU5lrAUgVEp4if6/WJThUpoxVFImWUSEMuF0Ry7DYRNUIWs32tj6cJczKjCYhrJ471tI5IkoBdi8TxkWXajoUaB28lzDm+BmeISCmutMsBgF+AvXBi2xxpTHex20SkYxeOAlVlgF+wc1cAnRXfUdzqaR3R+YNi00BAJCcRqc2rBRFb5khhR6wbsjpbEAHV2+aINMTYbSJy3tl0cRvfQczsKEUOVjiro4Lo4G/249yj1vUdRCqSI7fVTpgDGKpAyso7DuQcAgxGIGWI88/jXkSkE4zdJiLn2dYPdVT2vLYZIh21zB38vfrnp3cCacO0GQs1DrYZohj1X8u2huiCCDMxMmOJPCDPDiX2BoKjnH8ek+ZIJxi7TUTOk1vamnZW9rzyDFHOIaCqQvSWa6nkAnB8gzhu3gM4tR04vYMFEalLnq3xZsucZAHK89VPtaOGLcONdjmALXOkG05dEnrooYdQXl5u+3zNmjWoqKiwfV5UVIS2bdsqPzoi0he5ZU6phDlZZAsgIFwkFOUcVvbc7ji8DJDMYuaqwwTxtdM7tR0TNXxyylyIF1rm/ALF/zmAbXPkGYsZyFgpjlsNd+25TVqL24KTIrabSCNOFUTvvvtutYLo6quvxpkzZ2yfm81mHDlyRPnREZF+lBfaQwaUSpiTGQz62qBVXj/UdgzQrLs4ZrACqc2boQoAEGKdFWJBRJ44vUPEtwdGAIm9XHtuSAwQGieO9XAxjBotpwoi6aINsy7+nIgagXP7xW1YAhCqwhs2W0Gk8ToicxVw+E9x3HasaJkDgAtHgLJ87cZFDZ/XCyJGb5MC5PVDqUPd2z+LbXOkA04VRAaDAQaDodrnRNTIqNUuJ9NLsELWBlH4BMcALfqIK5iRLcV9p3dpOzZq2Iq9XBA5BisQucsWt+1iu5yMSXOkA06FKkiShKeeegqBgYEAgJKSEvz73/9GREQEAKCsrEy9ERKRPtgS5tQqiHTSMie3y7UZbY8Wb94NyD8uWkNSXYiUJXKFPFOjxgxsTThDRJ4qLwSyNorjNHcLIibNkfacKoimTJmCkpISlJSUAABuueUWmM1m5Obm2h4zefJkdUZIRPqgekFknSHKzQQqioGAUHVepz5y3HbbMfavNesG7PuZ64hIPZWlQGWxOPZayxw3ZyUPZa4DLJVAVDIQk+beOdgyRzrgVEH0ySefqD0OItIzSQLOqVwQhcWJdK2S80D2ASCxpzqvU5ecI+IqpdEPaD3C/vVm1nVEp3d4f0zUOMjBBkZ/sTjdG7g5K3nKMW7b3eUUcstczmGRWKfkpt9ETuJObERUv4KTYl2N0c9+NU8NWgcryLNDyQOBoEj715t3F7c5h4GyAq8PixoBW+R2E/ffWLpK3nuIM0TkriPLxa2764cAIDIJ8AsCzBVA3jFlxkXkIhZERFQ/uV2uSRuxf4la5NknrdYR2eK2x1b/emgsENFCHJ/Z7d0xUePg7YQ5x9cqza37cUQ1yT8hZtQNRpEw5y6jyb4fEdvmSCMsiIiofmqvH5JpOUNUlg8cWyeOLy6IAPssEdvmSA1y25q8rscbGKpAnpDT5RJ72Wcb3cWkOdIYCyIiqp/XCiINo7ePLAcsVWIWrEmrS+/nBq2kpmJry1xorPde0xaqwDVE5Aa5Xc7ddDlHTJojjbEgIqL6easgimsvbgtPeb+Np6Z0OUecISI1adkyV5IjglOInGWxABkrxXGryz0/H5PmSGMsiIioblXlQI71l5TaBVFQhFhgC3h3lshiBg79IY5rapcDRPQ2IH5hlxd6Z1zUeGhREMkbs0pm0TJK5KwzO8WGvgHhQIvenp+PLXOkMRZERFS38wdFK1lQJBCRqP7rabFB64kt4g1pUCTQsn/NjwmLB8KbA5AYrEDKs6XMebFlzj8I8Lfu91XKtjlygdwulzoEMPl7fj45VKEkByjmmjbyPqf2IbrzzjudOtnHH3/s0WCISIfkdrn4Tt6JA47vIGZrvDlDJKfLtR5Z9y/35t2BA6eA0ztFNDeRUrQIVZBfL79YvL67G2tS43PEYf8hJQSEiu6A/CzRkRDqxZlSIjg5Q5ScnGz7kCQJX3zxBUpKSpCQkICKigosWrQI5eXlao+ViLTgrfVDMi2CFWzrh2ppl5MxWIHUokXLHMBgBXJdRTFwfIM4VqogAtg2R5pyaoZo5syZtuNevXrh22+/xfjx421f++233/DMM88oPzoi0p7XCyKHljlJUn9WKu84cG6P2Euj9ci6H8tgBVKLFilzAKO3yXWZ6wBLJRDZUtlZxdh2ohWPBRFpwOU1REePHkVKSkq1r7Vo0QIZGRlKjYmI9MTbBVFsW1GclOYCRWfVfz15diipf/3tSvIM0fmD4iopkRIkSbsZIjlYgWuIyFny+qFWw5W9YGWbIWLSHHmfUzNEjm677TZcffXVePDBB5GUlIRTp07h3XffxS233KLG+IhIS8U5QNEZcSzP3KjNP1hcdcw5LGaJwhPUfT15/VBtcduOwpsCYQniz+TM7toDGIhcUZYvkt4Ae4HiLZwhIlfZCiIF2+UA7kVEmnK5IJo9ezZat26NH374AadPn0Z8fDzuu+8+zJgxQ43xEZGWzllnh6JTgMBw771ufAdrQbRP+V+6jsqLgKOrxXF964dkzbuLIurUDhZEpAy5GAkIE8lv3sQ1ROSK/JPA+QNiFj91qLLnlgui3Eyx3YNfoLLnJ6qDywWRn58fHnroITz00ENqjIeI9MTWLtfZu68b3xHY97P60dsZKwFzhSj44to595xm3UVBdHqnigOjRkWrdjnH1+QMETkjw5ou17yH8omIYfFAYCRQng9cyPBeVwIR3NyH6IsvvsDNN9+MQYMG4dChQ7jxxhtx/vx5pcdGRFqzRW539O7r2oIVVE6as7XLjXW+F57BCqQ0LQui4GjrGDhDRE5Qq10OED+DmTRHGnG5IPrHP/6Bp556Cmlpadi5cyeMRnGK++67T/HBEZHGvB2oIIu3vt65/YDFos5rWCxivyPAufVDMjlYIXs/UFGi+LCoEdLDDBFDFag+FouYVQfUa2XmOiLSiMsF0YcffojffvsNL774Ivz9/eHv748XX3wRS5cuVWN8RKQVi9k+Q+PtlrmYNMAUAFQWA/nH1XmN09tFil1AGJA82PnnRTQDwpoCkgU4m67O2Khx0SpyG3BYQ8SWOarHmV3i30lAGNCijzqvwaQ50ojLBVFUVBSOH6/+BiUnJwdNmzZVbFBEpAMXjgJVpYBfMBCT6t3XNvmJPSkA9drm5LjtVpcDfgGuPZcbtJKS9DBDVHJBxH8T1UZul0sZApj81XkNzhCRRlwuiP75z39i4sSJmDRpEsrLy/Hmm29i0qRJ3JiVqKGRE+bi2wNGk/df33GDVjU4rh9yVbNu4pbriEgJ8vodpRepO0OO+bZUAuWF3n998h1yoIKayZ+2gugQC3TyKpcLosmTJ+PPP/9EaGgoLrvsMhQVFWHBggW4/fbb1RgfEWlFq/VDMrkgOqtCQVRwypoSZwDajHb9+XKwAmeISAkl1pa5EA1a5gJCxCwwwHVEelZVDiy4SnxoUbhWFAPHN4jjVsPVe52YVMDoB1QUAYWn1Xsdoou4HLsNAEOGDMGQIUOUHgsR6YktYU6rgsiabKdGy5zcLteiNxAW5/rzHYMVKkvFZrJE7tKyZU5+3YITYhzRKdqMgeq29k3g6Cpx/M0dwC2LRGuxtxz7S2xREJkENGmt3uuY/IHoVCDnkGibi2iu3msROXB5hmj37t0oKWGyElGDp5cZovMHAXOlsueWCyJX0uUcRTQHQuMAyWz/cyJyl+YFkRy9navN61Pdco4Aa14XxwYTcPhPYMmT3m0pOyK3yw13fosCdzm2zRF5icsF0ZgxY7B582Y1xkJEelFeBOQeFcdaFUSRSSLNyFIp3hAopbLUHh3rzvohQLwhsAUrbFdiVNSYFVsLIi1S5gBuzqpnkgT88jhgLgdajQBuXAjAAGz5GPjrLe+NQw5USFOxXU7GvYhIAy4XRFOnTsWCBQvUGAsR6YXcphbWVLs3aUYjENfeOh4F1xEdXS3S8yJaeBYnzg1aSQnmSqA8XxxrNUMUrIPo7ZPbgEN/avf6epX+nQgzMAUCV74KdBgPjJ0l7vvzX8Ce/6k/hoJTQPY+AAYg7TL1X49Jc6QBlwuiESNGYP/+/Rg3bhyWLFmC1atX2z6IqIE4p3G7nMyWNKfgOiJbutwYz1o/bDNEOz0eEjVicsKcwQgERWozBq03Z7WYgc+vBz6/gW1Sjsrygd+fFsdDnwCatBLH/acD/aaJ4+/vA45vVHcccrtc8x7eSUJkyxxpwOUVeXfffTcA4PTp07j//vttXzcYDMjIyFBuZESkHa3XD8lswQoKzRBJksP6ITfb5WRy9Hb2PqCyDPAP8ux81DjJszLB0drE2wPat8yd22d/7aOr7C1Tjd3y/4jNo5u0BgY9XP2+MS8CeceBA78CX94M3L3UXjApLcNh/ZA3xFpDGwpOikS9wHDvvC41ai7PEB09erTGDxZDRA2I1glzMqVniM7sFr9k/UOA1KGenSuyhXgjaalisAK5T8vIbZl81b9EoxmiE5vsx8f+0mYMenNyG7DpQ3E8bjbgF1j9fqMJuO4jMWtTekHMsBWrUNBaLA6BCiruP+QoOBoIjRfHnCUiL3G5ICKiBk6S9DdDdCFDhCF4Sp4dSrvM8xkdx2AFriMid2mdMOf42lrNEGU5FESZ67ghp8UMLH4UgAR0uRFIG1bz4wJCgVu+AiJbip+RiyaJ2Wolnd0tinb/UKBFX2XPXRe2zZGXudwyV1xcjPfeew8HDx6E2WwGAEiShB07dmD7dqYtEfm8glNAWZ6Id41rp+1YwuLFgu/SC0D2AXuQgbsc1w8poXl34MgyFkTkPltB5IW1GbUJtsZul2oUu+1YEBWdEW/u1Wr/8gWb54mfKYGRwJgX6n5seFPg1m+AeaOBrA3Aj/cD134kQmmUIM8OpQwG/AKUOaczYtsAx9YyWIG8xuX/Mbfffjt++OEH7NmzB0eOHEFcXBy+++47DB/upd5SIlKXPDsU2+bSNg1vMxiU26C16Bxwcqs4bqNQQWQLVtihzPmo8dE6chvQdoaoOAe4YI3Vj7O2yDbmtrmC08Cyf4vjkTPFRaH6xLcHbvoUMPqLVLrlzys3Hjlu21vtcjImzZGXuVwQLVu2DN988w2ee+45+Pv74+WXX8b777+PPXvYQ0/UIOglYU7WVKFghUN/AJBEERPRzNNRCfKM1bl9QFW5MuekxkUXLXMOsdveblc7Yd3XsEkboP2V4vjYOu+OQU9+fxqoKAQSewO97nD+eWnDgKveFsdrZwNb53s+looS4Ph6caxZQcSWOfIOlwuiyMhIHDhwAP369cP27dthNpsxfPhwrF+/Xo3xEZG36WX9kEypYAVbu5yH6XKOIpNEu5GlksEK5B5dFETW1zZXABXF3n1tOVAhqS+QPFAcN9aC6PBSYM/3IoJ9/GzX29663wIMe0ocL35MnM8Tx/8S/yYiWng/+U9+vQtHAHOVd1+bGiWXC6Knn34aY8aMQVVVFYYMGYLLLrsMN9xwAzp39mCDQyLSD1tBpJP/00q0zFWV23vh2ylYEDFYgTylh5Q5/xCx8Sfg/bY5ef1Qiz5AUj+xdjHvOJCX5d1xaK2yFPjlCXHcb5o91t9Vlz0FdL0ZkMzA11NFsqa7bOlyl3m2Z5s7IpMAvyBRkOUd8+5rU6PkckE0bdo0bNu2DaGhoZg/fz5GjBiBAQMG4Ouvv1ZjfETkTVUV9p5tuRDRWlx7cVtwAijNc+8cmWuBiiIgLAFIcPONRm3ktjmuIyJ36GGGyGDQZnNWc5WIlwbEDFFguL0QaGzriNa+AeQeBcKbA8Ofdv88BoNonUsZIlrvPr9RBOW4Q6v1Q4CYHWtinSVi25xycjOBN7sCn4xrfP/H6uFWDEnnzp0RGBiIqKgoPPvss3j11VfRokULpcdGRN52/qDYVycwUuyzowfBUUBEojjO3u/eOWybsY5WLn1JZpsh2qnsealxkPf+0TJlDtAmWOHcXqCyGAgIt1/4aIxtc+cPiYIIAK54yfONSP0CRMhCbDug8JQoisoLXTtH4Rnruk0DkHqZZ+Nxl9w2p7dgBV+Ohd/wvphxO7YW+OQK4LPr+LvLyuV3BqmpqUhLS6vxg4h8nOP6IW+3SNTFto7IjWAFSQIOLhHHSq4fktmCFfaKGTYiZ0kSUGxtmdMyZQ4AQqzR2yVejN6W1w+16CU2GgVEvDPQeAoiSQJ+eUy0hrUeBXS4SpnzBkeLOO7QOLGX0DdTXVuLI7fLNesGhGo0e6nHpLlv7gBeb+/+rJuWKkuBnV+I49ajAKOfWGf2wVDg6ymNfibO5YJo/vz5+OSTT/DJJ5/g3XffxdSpU1FVVYX//Oc/aoyPiLzpbLq4baqTdjmZJ8EK2fvFmgRToNiQVWlRyUBQlHhD42kSHjUuFcWA2ZpOqGXLnOPre3OGKMuaMOe44WfL/gAMQM5hoPCs98aild3fAEdXi/UyV76q7IWo6GRg0leAX7B44/vr487PbmjZLieL1VnLXMFpEXpRdEbsFeVr9vwPKMsXG/lO+gp4cLPY+BcGYO8PwLt9gR8faHzr96xcLoiGDRtm+7jiiivwr3/9C0uXLsWHH36oxviIyJvkN/R6SZiTeRKsIKfLpQ4VO7srzWCwzxIxWIFcIRcffkEi2EBLwdaWPW+uIXJMmLONI9r+8+d4A1/jUJorYrYBYOiTQEyq8q+R2Au4fh4Ag4jiXvff+p9jsQAZcqCClgWRzmaI5E4DANi2wPe2WtjyibjtNUXMyMakAdd9CExfB7S7EpAswPbPgLd7AkueAoqytR2vlynSTJ+SkoLMzEwlTkVEWtJbwpxMniE6u8f1/u0Dcty2Qpux1kReCM5gBXKFLVAhVvsWVW/PEBWfBy5kiOMWvavflzxI3GY28La5Zc8Dxdlirc/Ah9R7nfbjgLEvieOlM4H07+t+/Lk9Ylz+IdWLVW9r0lrcll6wb2Cspf2/2I+Ls4G9P2k3Fled3SMuQBj9gB63V7+vaSfgli+Bu/4UYRzmCmDjHOC/3YDl/xGzSo2An6tPuOOOO2Bw+MFtNpuxefNmtGvXTtGBEZGXlVwACk+LY7kA0YvYdgAM1l+M2c7t3g6IX6LyVWg11g/JGL1N7rAVRBoHKjiOwVsFkbwha2xbMSvkKHkgsOmDhp2CdWILsOVjcTzudRGEoKb+00TC2MY5wP+mARHNre2JNZDb5VIGA36B6o6rLgEhor0r/7iYJQodoN1YygtFayMAdL4OSP8O2PwR0PUG7cbkCnl2qN2VQHjTmh+T1BeY8rOYHVz2b+DUdmD1q+L7HPwo0Oce8XfSQLk8Q5SSkoLk5GTbR6tWrfDEE0/g++/rueJARPomzw5FJXuecqS0gBB7O4kr63QOLxVtAE07A1FJ6owNsLfMnd0DmCvVex1qWPQQuS2zzRB5qWXOtv9QDTMQ8gzRuT3eG483mauAxY8AkIButwCpQ7zzumNeANqNE+vWvrwFyDlS8+P0sH5IppekucNLxcxJk9bAmBfFTEvWBs/2efKWimJg11fiuPcddT/WYBB/7/esAG60JhWW5gJ//gt4q4dYO9VAf8e5PEM0c+ZMNcZBRFrTa7ucLL6jaLE5t8/5cISDXmiXA4DoVCAoUrQWnNsHNOuq7utRw6CrgkieIfJSASLPECX1ufS+sDgxc3T+IHB8vWj5akg2zRVvpIOigFHPe+91jSaxZmT+eODUNuDz64G7llZPkassBY6tF8dpw703ttrEtgWOLNO+IJLb5dpdCYQnAB0miJCCzR8BE5xYl6Wl9O+A8gIgOsX5CHWDAeh4lfi/t+srYMUsMVP3y2PAX28Bw/8hZsrkdMgGwOWCaPjw4dVa5mqyfPlytwdERBrRa8KcLL4DsH+x8zNE5krg8DJx3PYK9cYFiF8ezbqJlorTO1gQkXP0ErkNeDdUwVwFnNwqjmuaIQJE29z5g6JtriEVRAWngBUviONRz4niz5sCQkXC2EcjxAWmRZOAyT8C/kHi/mN/iRmk8OZAnA6WQughac5cCRz8Qxy3Hy9u+9wtCqJdXwMjnxP75emVLUzhDtf34TOagO6TRPGzdYFoocvNBL6/R+yddfk/RZGo9RpIBbjcMtemTRukp6djwIABuPnmm9GnTx/s3r0bnTt3xpQpUzBlyhQ1xklEatNrwpzM1ejt4+uB8nyxYD2xp3rjksnriBisQM7S1QyRF0MVzu0BKkuAwAj7hqwXS26g+xH99hRQUSQKwR6TtRlDWDxw67diVjtrA/DDdJEsB1Rvl9PDm1w9JM1lrhW/S0Lj7AEgyYOAuA7i3/HOL7UbW31O7xSzgUZ/oPut7p/HLxDody/w8A5gxL/Ev51ze0VBPW+UfX2VD3N5hmjdunX4+eef0a9fP9vXJk6ciHvvvRdvvfWWooMjIi+xmO2Fhp5b5gAxTkmq/5f1wd/FbZvR3pnWZ/Q2uUqPoQpVZUBFibqLp+X1Q4m9ar9inWxdQH96J1BWAARFqDcebzn4B7D3R8BgAsbPdv1qvZLi2gE3fQZ8eq3YWyc6GRj5LJCxUtzfSgftcoC9IMo7BlSW2WeyvOnAr+K27Vj77xKDAeh7N/DL46Jtrt80fRSQF5NnhzpMUGY2MiAUGPI40PtOYN1bwMb3RfvrggmilX3Ev8T/ax/k8v/G7OxsWOQrCVYWiwXnzp1TbFBE5GW5meJKl1+Q2JtAj5q0Fle5KoqAfCc2jjtg3TOinYrpco7kGaIz6Q120SkpzDF2W2sBYYDJmnSm9iyRbf1QHZHOkS1EwItksRdQvqyiBPj1CXHcfzqQ0EXb8QBib7ar3xHHa98AVr5sb51WYxNrd4TFA4GR4t+BHNPuTZIE7LcWRHK7nKzrTUBAuNhEWC4k9aS8UGz8C9QfpuCq4Ghg5EzgoR1A33vF7+aMlcCHlwOLbgXO7Vf29bzA5YJo+vTpuPrqq/HYY4/htddew8MPP4yJEyfiwQcfVGN8ROQNcqBCXHv9LpI0+duvFtbXNnf+MHDhiPgh7a2FwdGpogXIXA5k+94vA9KAnlrmDAb7OiK1C6K6EuYcpchtc2vVHY83rHlNzHJEJAKX/V3r0dh1uxm4zLo57MoXxW2zbvpY1waIf5daJs2d3gkUnBB7MqUNq35fYLj48wPELJHe7P5GXEBs0lrsL6SG8KbAla8CM7YA3SYBBqNY6ztngM/tI+ZyQfTcc89hzpw5yMnJwbJly1BYWIh58+bhmWeeUWN8ROQNek+Ykzlu0FoXOV0uZZD3Wm2MRvsGrad3euc1ybfpqSAC7ONQM1ihKBvIPSqOW9TTWpM8UNz6+n5E2QdEexEAXPEyEBim7XguNuz/xJtZmR7ith3Z1hFpEKwgt8u1HgH4B196f5+77I/LP+G9cdVHkqqHKajdzhedAlwzB5i+XrTnNWkDJPWr92l64vIaIgC47rrrcN1116GyshJlZWUID9fZniVE5BpbwpxOAxVkzgYr2OK2vdQuJ2vWDchcI4IVetzm3dcm32Ix2yOu9XI13hvR27YNWdtduiHrxeT9iE5uU39dk1okCVj8GGCpFD+PLm670gODQURHF50VbU+drtF6RNVpOUNki9uuJekwvoOYfclcIwqQETqZHDi1DTizCzAFipQ4b4lvL9amlRcBJrdKDM04NUNUWFiIxx9/vFpb3DvvvIOoqChERUVh8ODBOH78uGqDJCKV2WaIdBq5LXMMVqhNaZ79irLa+w9drHkPcctgBapPaR4ASRzXVxh4i1cKImu7XF3rh2TRKSL+2VJpL6R8zc5FouXPLxi44hV9LrwHAL8AkTz3fxn2mW690CppLjdTXCw0mOr+XdLnbnG7bQFQVe6VodVLnh3qeLU2oS16mwV1glMF0V133YX169fj6quvBgD8+uuveOyxx/Dee+8hMzMT7dq1w/3336/qQIlIJeVF4gc/4Dstc+cPiL1ManJkGSCZxRVobwdEVAtWqGV8RIC9XS4oUqyP0wNvRG9nORGoIDMYfLttruQC8Mc/xPFlfxNJbnpmNOpzPx3HljlJ8t7rymEKyQPrLirajwPCmwHF2cC+n70ztrqU5YvNWAHlwxQaMKcKoj///BNffPEFRo0aBQB46qmncP/992PKlClISkrCs88+i7VrG8CiR6LGKHs/AAkIa6qf1p3aRCWLxa3mitoTh+S4bW/PDgGiAAsIB6pKRdFGVBs9JczJ1N6c1VwlWnmA+gMVZCnWtjlf3I9o2XPi7zmuPdD/Aa1H47tiUgGjH1BZLDa29RZ5/VC7K+t+nMkf6DVVHG/6UNUhOWXX1yI1NrYd0HKA1qPxGU4VRAkJCThwQPxynzdvHg4fPoynn37adv/+/fsRFRWlygCJSGXy+qF4nbfLAeIKpryRo7yRrCNzFXDIuqN4uyu8Ny6Z0Qg06yqOuUEr1aXkvLjVS6ACoP4M0dl08UYtKNJ+1b8+8jqiE5v1047kjOMbga3zxfG42aIljdxj8rfP9nurba7kgr0Ib19PQQQAPaeIoi1rA3Bmt7pjq4tjmELvO/XboqlDThVEr732Gm666Sa0b98e06ZNw6uvvor4+HicOHECd955J66//no88sgjKg+ViFRx1lpY6D1QQVbXOqITm4HSXCAoyvkr0EqT2+a4jojqoreEOUD9NUTyOqDE3s5vShrbVsyiVZUBp7arMy6lmSuBxY+K4+632We5yH3eTpo7+LvY+6hpZ7GWrT4RzeyBGVpGcJ/YDJzbI/YU7HaTduPwQU5FQIwbNw579uzBxo0b0bp1a3TtKq6Ams1mREREYNGiRbjiCg2uxhKR53wlcltmS5qrYYZITpdrM0q7hJvm3cUto7epLrosiFSeIcpyIVBBJq8j2veTuGLfsr86Y1PSxvfFm9LgaGDUv7UeTcPg7aS5A9Z0ufa1pMvVpO89wN4fRMvayOe0WY8lzw51ulY/YS0+wul9iBITE3HttdfaiiEASE5OxptvvulWMZSTk4O//voL58+fd/m5RKQQSXKI3PaBljmg7uhtreK2HdmCFXaLaGWimhRbi45QHRVEwWrPEMkbsvZx7Xly25wvbPSYfwJYMUscj/q3vv5+fZk3k+YqS4HDy8RxfeuHHCUPAuI6iLbQnYvUGVtdSnOBPd+LY4YpuMzljVmVsGjRIrRu3RoPPPAAWrZsiUWLxD+c9PR09OnTB9HR0XjyyScheTNNhKgxKjwNlOWJWNHYdlqPxjlyy9yFI0Blmf3rF46KgAiDSWyip5UmrYGAMPFLUYt9M8g36HKGSMVQhaJsa5qlAWjR27Xnyi1nWRv1n9645G9i8X9Sf9EuR8rwZstcxirx8zuihWsR5AaDfaPWzR95NxEPEEVYVRkQ38n1iw7k/YIoLy8PM2bMwJo1a7B9+3Z88MEH+Nvf/oby8nJMmDABvXr1wpYtW7B3717Mnz/f28MjalzkdrnYNoB/kLZjcVZ4glgjJFmqFxxymELLAdq2ChiNQAKDFRRVXtTwZtt0WRBZx1JZIq6SK0meHYprL0IVXBHfUTynogg4o+NW1ANLgP2LxeL68bOdXydF9WvSWtwWngLKC9V9rf2LxW37K10PJeh2s7gglnNIbHLrLdXCFO5gmIIbvP6/tbCwEG+++SY6dxbrFbp164bc3FwsWbIE+fn5mD17Nlq1aoUXX3wR8+bN8/bwiBoXX0qYkxkMNQcrHFgibrWI276YfFWRwQqey1gFzGoBvJwCfHYdsPpV0Tql9Bt2b7OlzOkodjswXLyZB5Rvm7OtH3LjyrXRZI8P1ut+RBXFwK//J44HPOA7ITW+IjhKbA0BqDtLZDHbW69daZeTBYaLogjwbrjC8fViqwf/EKDrjd573QbE6wVRUlISbr31VgBAZWUlXnvtNVx77bXYuXMn+vfvj5CQEABA165dsXdvDYumrcrLy1FQUFDtg4hc5GsJczJ5vZMcrFBeCGRa90LTIm77YnKwAmeIPLdlHgAJKC8ADi8Flv8HmH8lMCsJ+GgU8Oe/RDGs1roXtehxhshgUC9YQU6Yczf9UV5HpNeCaMcXQP5xIDIJGPY3rUfTMHmjbe7EFrHBamAkkDLYvXP0uVvcHvhVrCnzBnl2qPN1rs/AEgCN1hABwM6dO9G0aVP88ccfePPNN1FQUIDU1FTb/QaDASaTCbm5uTU+f9asWYiMjLR9JCUleWvoRA2HryXMyS4OVjiyArBUir0q5NYKLdmCFXY1vFYvbyovAg5aWyGv/Qi44hWg40RxpdhSKdqw1v0X+PJm4JVU4N3+wM+PiJSnvONajrx+cgEnr9vRCzU2ZzVXAietG7K6kjDnyLEgsliUGZeSdnwubgc8CASEajuWhsobSXNyu1zb0WL/I3fEdwBShoi2bnkvKjWVXAD2/iiOGabgNs0Koq5du2LZsmXo1KkT7rjjDvj5+SEwMLDaY4KCglBSUlLj8//+978jPz/f9pGVleWNYRM1HFUVYood8J2EOVn8RTNEjulyeuidjm0D+IeKtRg5h7Ueje869DtQVQpEpwJdrgf63QfcuAB4/ADw0A5g4hyg52SgifWNUvY+YOsnwPf3AG92AWZ3Ar69S+wef3aPft5IV5aJ9TAAEKqjljlAnRmis+ni7zEo0v535apmXcX/qbK8miP3tXR2r9gjyegPdLlB69E0XGonzUkSsN8at+1Ou5wjOVxh6wLxu1ZNO74AzOVi7Wrznuq+VgOm0UYdYgaoR48emD9/PpKTkzFr1iykp6dXe0xhYSECAmre3TkwMPCSAoqIXJBzCLBUAYERos3Dl8S1F7f5WUBpnthED9A2btuR0QQkdBG7lp/aAcT5SIKf3qRbI2Q7XVO90DUYgJhU8dF9kvha8Xng+AbRS398vdgHquAEkP6t+ADEG/KkfmI9SssBQGJPwE+D3yNysWH0E///9CTEGkiiZAtiltwu18f9oAGTP9CyH3BkuZglStDRrPbOL8Rt2zGM2VaTbYZIpZa58wdFeqkpAGg90rNztR8PhDcTSa77fhIXdNQgSeIiEAD0vlMfFwR9lMs/mb7//nukpKTAZDLZPoxGI0wmk1PPX758OZ588knb535+oiZr3749NmzYYPt6ZmYmysvLEROjs3YCoobC1i7Xyfd+iIbEiF82gLg6VnJevLGUF17rgW2D1h1ajsJ3lRcCh/4Ux52vrf/xobFAh/HAmBeAe5YDTx0HJv8EXPY0kHaZdXYhX6QRLnsO+GSsWIf08Vhg6bNi3yhvcVw/pLf/e7YZIgULItv+Q262y8mSB4rbY2s9O4+SzFXAzq/EcfdbtR1LQyfPEF04ok78ujw7lDoUCPLwQoXJH+g1VRyrGa6QuUZ0IQSEqVd0NRIuF0TTp0/HTTfdhP379yMjIwMZGRk4evQoMjIynHp++/bt8cEHH2Du3LnIysrCU089hdGjR2PcuHHIz8/HwoULAQAvvfQSRo4c6XShRUQuSv9O3Dbvoe043CWvI1r/jrhtdTngV/OMsibkdUQMVnDPgd9EG0iT1u6tcQsIBdKGAZf9DZj8oyiQ7l0JjJkFdLgKCI0T5z++Hlj7BvDJOPXjfGW2gkhn7XKAw+asCrbMeZIw5yjZusj92F/e3+OlNkeWAcXnxN9lm1Faj6Zhi2gB+AUD5gog75jy55cLovbjlDlfzyliFvj4euBMev2Pd4ccptDlBpFwR25zuSAyGAy499570aZNGyQnJ1f7cEbz5s3xzTff4M0330SnTp1QUlKCTz/9FH5+fpg7dy6mTZuGpk2b4ttvv8VLL73k8jdERE44ky7W3RiM9kQcXyOvIyo4KW71kC7nSI7ePrNLP2tXfMmeWtrl3GXyE8X/gPuBmz4FnjgEzNgGXP0uENkSKM/33u7ytoJIhx0Q8gyRUqEKReesb14NQKKLG7JeLLEnYAoUKWB6WZsnhyl0vcn9RfjkHKMRiLWG5ijdNld4Bji5RRy3Veh3SUQz0ToHAJs/VOacjoqygX0/i2OGKXjM5YLoP//5D2bMmIHz58+7/aJjxozB3r17UVBQgG+++QZxcXEAgIkTJ+LQoUOYO3cu9u3bh06dfCwKmMhXrH1D3HacCDRppelQ3CbPEAEADEBrnV2djW0rrmZWFOnnzZuvKMsXEduAKIjUYDCIf/s9bgMGzhBf2/Shd2Ye9Bi5LVM6VEGeHYrv4Hkbkl+gWIcE2GP2tVRywb7/mbyWjdSlVrCC/PeY2FsUMkrpe4+43fW1+LmmpB2fi7TN5j3tF+DIbS4XRJ9//jm2bNmC5ORk9O7dG5dffrntQwmJiYm4+uqrbUUSESks54j96vuQx7QdiyccC6KkvvpbzGzyE8EKgFjgT847sES0xcS2886mwfLu8ucPAEdXqf96ui6I5JY5hWaIbOuHPGyXk6XoaD+i9O/Ev9OErvoKeWjI1CqIbO1yHqbLXSx5EBDXQSSO7vhSufNaLNXDFMhjLqfMTZ06VYVhEJHXrPuv2B+hzWj7G3ZfJCfNAfpJl7tY8+7iDeHpHUBXxvE6bc//xK1S7XL1CYoQV/g3zQU2zhUhDGoqtnZY6C1yG1A+VEFOmHN3/6GL2YIV1onZPC1DKeR2OYYpeI8aSXPlhfYLIe0UWj8kMxhEBPevT4hwhX73KfNv9uhKIDdThAk5EzpD9XK5IJoyZUqNX6+oUDlnnYg8V3BKpLIBwJDHtR2LpwJCxVXn07vEInk9YrCC60pzgcPLxHGnid573T73iILo4BIg9xgQ7dy6WLfoeYYo2Bq7rcQaInOl2J8H8DxhTtaij1ioXnBSrE2KTlHmvK7i3kPaUGOG6PBSMdMX00qdLRK63iSSLHMOicJLiQsucphC15u4EbBCXG6ZO336NB544AGMGjXK1io3fPhwp0MViEhD698VPcfJg4CW/bUejecmfQ08sMG+0FZvbNHbOxms4Kz9v4p/o3EdLlonprK4tkDacDF7umWeuq+l54JIHlNFkdhA1hNndls3ZI0SaYFKCAi1bz6pZdsc9x7SRkwrAAZRsBcrtM5t/6/itv2V6sw4BkWItlxArFP0VOFZ4IB1zAxTUIzLBdFtt92Gs2fPIjg4GMHBwbjhhhtw4MABTJ8+XY3xEZFSSi4AWz4Wx768dshRSAwQk6b1KGoX2w7wCwIqCoELzm1N0OjJ7XJatIH0vVfcblsIVJaq9zp6LoiCIgGDdbsLT2eJTiiwIWtN5La5zHXKndMV3HtIOwEhQJR1I/HzBzw/n7kSOGTd2FtOhFODnOZ64Fcg/6Rn59r+qdhUvUVfsY8gKcLln1CbNm3Cu+++iyeeeAL5+fmYPn065s2bh99++02N8RG5L/8kUMVWTpuN74uFnQldgVYjtB5N42Dys++hww1a61dyAchYIY47TvT+67cdA0S1FG17u79V73X0XBAZDMoFK9j2H1KoXU6WIu9HpFFBxL2HtKVk29yxdSL9LSRWueCPmsR3EPtoSQ5hCO6wmIGtC8QxwxQU5XJB1Lx5cyxduhR9+vTBnj17UFpais6dO2P3bi/u8k1Un4N/AG92BuYMAE5u03o02isvFAURINYOabkQubGR2+bktRRUu/2LxZXPpp1FC5u3GU32K7mbPlAngluS9F0QAcpFbyudMCdL6iv2UMs9KtZFetv2z8Qt9x7Shq0gUiBYQW6XazdW/P9XU1/rz5atC9y/WHtkOZB/XLShenONZSPgckE0a9Ys3H333SgqKsLEiRPRpUsXDB8+HIMGDVJjfESukyRgxX/ElZicw8C8UcDq18SVlcZqyyfiKliTNkCHCVqPpnGRgxUYvV0/x3Q5rfS4XbQ5ntkNZG1U/vxl+aLoA/RbEAVbZ4g8aZkrPAvkHYfYkLWXIsOyCYq0J2R6ex0R9x7Sni1pzsMZIklyiNtWsV1O1n48EJYgZhf3/eTeOeQwhW63AP7Byo2NXC+Irr32Wpw6dQrR0dGYO3cu/vnPf+LRRx/FN998o8b4SCsWM1BVrvUo3HNkmXjz6R8i3vxbqoDlzwPzx4n0qMamsgxY/444Hvyo+lfBqDpbsMIu72z66auKc4AMa/StlgVRSIw9NWzjB8qfX551CQgD/IOUP78SbC1zHswQybND8R0935C1Jskatc3t/laEfnDvIe0o1TJ3ZhdQcEK8V1A7ah8Qs4m9porjzR+5/vyCU8BB6/IUhikozq1VjtHR0fDz84MkSZgyZQqmT5+O8PBwpcdGWpEk4JMrgP92A4rOaT0a162ZLW573QHc+Ckw8X0gIBw4vh54f7BYDNuY3pju+BwoOgtEJgFdb9R6NI1PXHvAFAiU5zNYoS77fgIks3ij2aSVtmORwxX2/QQUnFb23PK6HLno0CNbQZTr/jls64dUWpdh24/IyzNE3HtIe3JBlHvMsyREeXao1eXem23pNVXExh9fD5xJd+252z4VPyNbDlQnHryRc7kgKiwsxL333oumTZsiJCQEu3fvRosWLbB161Y1xkdakFtFCk8Da9/UejSuObZeXDE0+gMDHxRrZbrfAkxfCyT1A8oLgP/dC3x3F1Cap/Vo1WeuAta9KY4HzmC/uxZM/vYryQxWqJ0e2uVkzboCLQeI2WVPFkDXxLZ+SIebssqUWENkS5hTOFBB1nKAuM3eb9/oVm1n94j/w9x7SFuhcaJtEhJw4Yj757HFbSu8GWtdIprZ2/NcmSUyVwHbGKagJpcLojvuuAMnTpzAwoULERoaisjISMyYMQMPPPCAGuMjLez72X68ZZ7yV0jVtNY6O9R9EhDR3P716BRg6q/A8H+ISNn074A5g4CjazQZptekfyf6+ENixdoI0kazbuKWG7TWrCgbyLT+X9RDQQTYZ4m2fKJsWmWJ9c27XtcPAZ6vIaqqsIeIKJ0wJwttItrxAO/NEu3g3kO6YDB43jaXeww4u1uEc7QZo9zYnCEHt+z6WqwpdMbhP8VmxMExQEedbkTu41wuiJYuXYq5c+dizJgxMBqNMBgMuP3227Fnzx41xkdakAsi/1CgqgxY+4a243HW6V3AoT/ED7hBD196v8kPGPZ/wF1/iL1rCk4ACyYAf/6rYcZzWyz2v7sB94v9G0gbtmCFHVqOQr/2/SRCUJr3AGJStR6N0GECEN5MLIDe+6Ny59V7whzg+QzR2d3id0dwtHIbstbEm21z5krxBhZgu5weeJo0J29s2nKg94vblMGilbqyGNi5yLnnyGEK3ScBfoHqja0Rc7kgat++PRYsENN2BoMBBoMB69evR6dO3ByqQTh/CMjeJ3pcr7HGNG/9xPONxLxhzevittO1da9BaNEbuG8N0HMyAAlY91/goxFAtgKbvOnJwSXi7zIwwn5FirRhC1bY2bjWrzlLT+1yMpO/vTVl01zlzisXGaF6bpnzMFQhy2FDVjUj/pOt6bbH1qr3GrLD3HtIVzxNmrOly12pzHhcYTDYfydv/qj+3wl5WWKGCBBro0kVLhdEb7/9Nt58800kJiaisLAQN910Ex555BG8++67aoyPvE2eHUodJq6QJg8GzBX2YkOvzh+yX8Ud/Gj9jw8MA656G7jpMzEFfWYX8MFQYNOHDeMNqyTZ/8763G3ttybNxHUATAGiPSI3U+vR6EvhWSDT+oZWi81Y69JzilgvcmKTcvtIFcszRHoOVZBniNxsmbPtP6RSu5xMniE6k67+mlA5TIF7D+mDJy1zJRfss4rtNCiIAPHvKCBMjP/oqrofu22hmEFPHQrEqjjj2si5XBD16dMHhw8fxiuvvIIXXngBDz74IPbu3YtevRTeZ4C0IRdEHSaIqxjD/y4+37bQuqeETq19E4AEtL3CtSjUDhOA6X+JlJmqMuDXJ4AvbvTNdD1HR1cBJ7eK/VT636/1aMgvAGhqnUVn21x1+34CIAGJvYHoZK1HU114U/us1aYPlTmnT7XMuVkQyTNEaiXMycITgJhWACTg+Ab1Xod7D+mPY8ucxeLacw/9IdLa4jtp16IbFAF0u1kc1xWuYK4U778Azg6pzKmCaOHChdU+fvzxR5jNZiQkJKCqqgo//vgjFi5cqPZYSW35J4BT2wAY7KkrKYPFbJGlUmxuqkd5WcAuax/ukMddf35EM+DW74CxL4t45EN/AO8NAA78puw4vUmeHeo5BQiL03YsJMjriBisUF369+JWT+1yjuRwhd3fKpNm5gsFUXC0uK0odH19ZeEZIP+4WMup9IasNUmR2+ZU3I9I3nuoWTfuPaQX0Smitb+yBCg85dpz9y8Wt95Ml6uJ3Da3/9falyUc/A0oOiOS9byxeWwj5ufMg6ZOnQqDwQCTyYSePXsiJMS+OFuythcZDAZMnjxZnVGSd8g9tS37A2Hx9q8Pf1rMOOz4XLSj6WXRs+yvt0U8bsoQ969IGo1A/2liSvr7e4Cz6cCXN4k1BKNf8K1AghNbgKOrxS+LgTO0Hg3JmncHtoIzRI4KTon9OACg00RNh1KrFr1F2MOp7eJK7ZDHPDufLWVOx2uIgqJEQSNZRNJceILzz5X3H4rvCAR6YX/C5EHi70XNYAXuPaQ/Jn8RjnT+oPiIbOHc8yrLgMPLxbEW64ccxXcQyxKOrQW2zgcu/8elj7GFKdwqOg1INU7NEL3zzjuYPn06Bg4ciMzMTJw6dQqxsbEYMWIEHnvsMcyfPx/Lly9Xe6ykNsd2OUct+wOtRoiiQ2+zREXZ9mx+d2aHLta0I3DPcmDAg+LzLR+LtUVKrR/wBnlj2q43AVFJ2o6F7ByjtxvCOjUl7LW2yyX1c/4NjbcZDPZZos3zxH4gnvCFGSKj0T5L5GrbXNZGcdtC5XY5mbyO6NR2oLxI+fM77j3U+Xrlz0/ucydp7ugqke4WkWiftddSX+ss0db5l87G5mYCR6zvrXtN8eaoGiWnCqL7778f77zzDlauXIkzZ85gxYoVGDlyJFasWIGJEyciLS0N0dHRao+V1FR83t5yUNO07PCnxe3OL4EcDzZCU9qG98Tan+Y9gbTLlDmnXyAw5gVg8o9AeHMg5xDw0UjRhmYxK/Maajm7FzjwCwADMOgRrUdDjuI7ijdVZXlA3jGtR6MPekyXq0mna0UBU3BCpDe6y1xp33dEzwUR4H70trwhq1r7D10sqiUQ2VKsCZHDHJQk7z3Ubiz3HtIbd5Lm5Ha5dleqm4DorPbjgbAEkWC476fq921dAEASa5xj0jQZXmPiVEH066+/4tVXX8XUqVPRp08fdO3aFR999BFSUlIwe/ZsLF26FEeO6OhNMrnuwK+iPaJZt5oXNrfoLTYvk8zAqle8P76alObZFyMOeVz5H25plwHT1wEdrxazY8v+LfYt0nW4hHXfoY5XAXFttR0LVecXKGYgARG/3djlnwCyrAvhO16t7Vjq4x8k1uMBwMYP3D+PPNtiMALBUR4PS1Xy5qyuFERVFfY1cmonzDlSaz8i7j2kb64mzVnM9nAMrdvlZCZ/oNdUcbx5nv3rVRXA9s/EMcMUvMKpgmj8+PF46qmn8Pnnn8PPzw9XXHEFOnXqBIvFgp07d+LTTz/FE088ofZYSU21tcs5khPndn8NZLuZ/a+kzR8B5QUi0lit6MyQGOCGBcDV74mIzGPrgDmDgV3fqPN6nrhwFEj/ThwP9nCdA6mDwQp2ckx+ywFARHNtx+KMPneJQiZzDXBun3vnkIuL4GjAaFJubGqQZ4hKXWiZO7MLMJeLYqquveCUJgcrZCocrCDvPRQaB7Qeqey5yXOutsyd2AIUZwOBkWLtjl70mgoYTMDxv0SLJiA6PYrPAWFNgXZXaDq8xsKpUIV//etfMOhhapHUUZYPZKwUxx2uqv1xzXsA7caJ/6irXgaun1f7Y9VWUSLa5QCxyNnocoK88wwGoMetQPIA4Pv7RFvG93eL9Jdxr+vnSu9fb4kZvNYj7RuBkr407y7WvDFYwaFd7lptx+GsyBYilWrfz2Kj1vFvuH4OX1g/JHNnc1Y5UEHtDVkvJm/QenKLWDTvH6TMeXdYr9Bz7yF9amLdk6fwNFBWIKKs63LAGhzVZpS+AgoimgEdxouLRJs+BCa8aQ9T6HE7/+15iVMF0bPPPqvyMEhTh/4Um6/GtgXi2tX92MueEj9U0r8Dhj4hUlK0sG2h+EUdley9N1QxacAdS8RaolUvA+nfil/AUxZrH15QeMY+vc7ZIf1ynCGSJH30sGsh77h1rYlBtHf6ir73iYJo5yJgxEzXL4bYCiIdJ8zJbAVRrvPPkdfwqL3/0MVi0sSV9KKzYv81ecbIE8U59q0Xut3i+flIecFR9r/3nEP1x7zLSbp6aZdz1OceURDt+loEKBxdBcDAMAUvUvGyOvkMeSFfXe1ysmZdrbNIErDyJVWHVauqCjEbAgCDHwFMTtX1yjD5AZf9Dbjzd7GYNzcTmD9O7IWkpfXviKI2qb+9n570J76jiEMvvQDka/xvRkt7fhC3yYNci3TWWspg8XdYWWJfbO8KW+R2jLLjUoM7oQryhqzeXD8EiAsLyQrvR5TOvYd8grNtc9kHgZzDItim9Sj1x+WqlMFAXHuRgPfV7eJrbUaJ9xnkFSyIGrvKUjFDBDhXEAFilggA9v4AnElXZVh12vUVUHBSJLN002jX8KQ+wB2/AdGpIjFswXixSFwLJReAzR+LYzXCJUg5/kH2WdXGvI7I1i43UdNhuMxgAPreI443fwhYLK49Xw5V8IWWOTlUwdk1RAWnRAqftzZkvZgtWEGhgoh7D/kGZ5Pm5Ha51KH1t9ZpwWCwb9QqXyxjmIJXsSBq7I4sF1c7I5Ocz+Rv2skek7tylmpDq5HFbE9SG/igcr3i7ohMBKb+IooieaZIi6Jo04fiqlLTLuKKEumb/P+ssa4jys0ETm0Tb5z1ni5Xky43ikXZFzKAI8tce6482xLqCy1zLs4Q2TZk7QQEhqkzprrIM0RZm0Q6nCfOpIskSO49pH/OJs3Z2uXGqTseT3S9SYQ3AWLLjzajtR1PI8OCqLGT0+Xaj3dtZmHYUwAMItPfm1e69/4IXDgidlLXw9WTyERg6mIgOkWboqi8CNg4RxwPeZSzQ75ADrxorNHb8uxQymAgLF7bsbgjMAzocZs43jTXtecWyy1zPjBDZFtD5OQMkW3/IS+vH5LFtRezWpUlnv9O2vmluOXeQ/rnTMtc4RmRMAeol0irhKAIoOdkcdz3bu8uByAWRI2audKeye9su5wsvj3QxXrlzFtriSQJWDNbHPefrs1VyJpEtrDOFKVYi6LxQP5J77z21vlAaa5YVNxxondekzzTrIe4lYMVGhtf2Yy1Ln3uAmAQ7caubFTtUylz8gyRkwWRLWHOy+uHZEajMm1z5krRlg2wXc4XyAVRzhHAXFXzYw4sASCJVs6IZl4bmltG/Vu8nxj0qNYjaXRYEKlBkoBDS0VKjZ5lrgXK8kTiUcv+rj9/2N9E28vBJSLZR22H/gTO7gb8Q4G+96r/eq6IbGFNm0sGco9aZ4pULoqqykWYAgAMflT/+5qQ0LSTCFYoOS/WwjUmOUfEzJjBVHfEv941aWVtT5Xsm0M7w5cKInkNUXl+/S1oVeX2FtAkjQoiQJlghcNLxV413HvIN0QkAv4hIgAj71jNjznwq7jV8+yQzOQvZs/V3EqEasQ/cTV8dzfw+XXAlo+1HkndbO1y49x7Mx3bRvS8AsAKldcSSRKw5jVx3OdOfaY0RSWJKztyUbRA5ZminV+K/RciEoGuN6v3OqQs/yCxmTDQ+IIV5Nmh1KG+sY6mLvJFme2fi9ZVZ/hUQRQFwNqCW1pP9PbpXSLlMqSJmK3WijxDdHyDWG/qDjlMgXsP+Qaj0b4fUU3riMoLgYxV4ljP64dIcyyI1NB2rLjd/KG4cqZHFotY/wN4dqV26JPiau/hP+0tE2o49heQtREwBQADHlTvdTzlWBRdyBBFUcEp5V/HXAWsfVMcD5yhr03mGpDvt51A55m/Y82hbGVP3KybuG1swQpy3LYvt8vJWo0Qb/7L8+0tVnWRJN8qiIwmIDhaHNcXrHBCow1ZL5bQBQiMAMoLgLNuJKBy7yHfVFewwuFlgLlc/F+Na+/dcZFPYUGkhk4TRUJI0Vkg/XutR1OzE5vF+AIjxNVadzVpBXS3/uJY8aIyY6vJmtfFbY/b9L9vSVSSCFqIaimKovnjlC+K9v4gZqGCY+yLMElRZouE1/84iKLyKiz4q5ZWDHfJwQqNaYbo/CHR8mr0c33Noh4ZjWIzRUAkPda3HqyyBKgqE8e+UBABzgcrZDkURFoymuzt35lutM1x7yHfVFdB5Ngux9AhqgMLIjWY/O17VWx4V58Lp+XNWNuO9Xx2YeiT4k1Oxgoxk6O0U9tFvK3BBAx8SPnzqyGqpXWmSC6KFJwpsljsBWL/+4GAUGXOS9Ws2H8OJ/NKAQBrD2ejtMLNFpyaOEZv6/Hngxrk2aG0y/TZ8uqO7pPEmsbsfUDmmrofKyfM+QX5zv9ZZ6O3bQlzGq4fknkSrMC9h3yTbS+ii5LmzJXAQeuMX/vx3h0T+RwWRGrpNVUs9DuzW4QX6Ikk2dcPKXGlNjrFHkOrxiyR/Oa/y/VATKry51dLVEsRtBDZUkSFzx8PFJz2/LyHfgfO7QUCwkU0J6nis432WaGySgvWHT6v3MkTOosCvzhbrANrDPZYZ8sbQrucLDgK6GZdR1lfBLdju5yvXKl2ZnPW/JMiHMRgBJr39M646mILVvjLtYsN3HvId8kzRNkHqv+dH/sLKMsXwVF6KNZJ11gQqSUkxt6DvOE9bcdysTO7RRqLXzDQeoQy5xzyhPhFkrkGOLpamXMCwLn99uJtsA/GUEYni/Y5uSha4GFRJEn2ArHPXfYef1LUsZxirDoo1g1d1i4OALB031nlXsA/2N7P3hja5s7tF0W80b/hLWyWwxX2/wLkZdX+OLntzJdmx5yZIZLXDzXVaEPWizXrLi5Gll4Asvc7/zzuPYTyKjPOFpRpPQzXNWkFwCBScx3/rcqbsbYdyxRWqhcLIjX1ny5uDyxxba8KtckFRusRyrVuRCUBvaaI4xWzlGsDWvemuG0/HojvoMw5vc2xKMo57FlRlLlWtKeYAkW7HKnii43HIUnAsLZxuHOQmJVctv8cLBYF29tsG7TuUO6cerX3B3HbanjDK+LjOwApQwDJUneyqG2GyIfS9ULkUIU6ZoiyrO1yWu0/dDG/APtaJmfb5rj3EADgn/9LR78Xl+HGD9Zj9cFsSL7SzusfLDoyAPs6Ikmyrx9qaBdhSBUsiNQU2wZoMwaABGyYo/Vo7JRsl3M0+DHxRv34X0DGSs/Pl3sM2PW1OB7ymOfn05KtKEqyF0WFZ1w/jzw71PN2ILypsmMkAEBZpRlfbRFX+m/vn4x+aTEIC/RDdmE5dp3MV+6F5KS5hj5DJEn2cJlO12o7FrX0u0/cblsAVNZyhb3E2nLpK4EKgHObs8ozRHpqSUoZLG6dDVaw7T0U32j3HjJbJPyWLn4nbTp6AZM/3oSr312H3/ecUfZCkFouDlY4sxvIzxKdMGmXaTYs8h0siNQ2wHoVf8fn9e/l4A3nD4sFwEY/oO0YZc8dmQj0vkMcr3jR81miv94CJDOQNlzsMO3rLi6K5rtYFJ3cJoIrfClcwgf9sus08koqkRgVjOHt4xHoZ8Kwtta2ub0Kts05Bis0ZOf2AecPiMj8dldoPRp1tL1C/L8uybGvlbqYL0Vuy+Q1RLW1zFWVi3U3gPYJc45swQpOriOy7T10Y6Pde2j/mQIUllchNMCEqQNTEORvxK4T+bjv060Y+9/V+GH7SVSZLVoPs3a2gsgarCC3y7UeAQSEaDMm8iksiNSWOgxo2llErm5doPVogP3W2aHUoeq0rgx+VKQondgk8v/dVXgW2PapOB7yuDJj04PoFGDKz9ai6BCwYILzRdHa2eK2642iuCJVfLpBhClM6tcSJqNY/D6yYzwAhdcRJXQRC9GLzioTtqFX8masrUZYN/tsgEx+QO87xfHGD2p+Ey4XFb60Ia1cvNUWqnB6pz42ZL1YYi9RgBedESmfdXHce6j7JPXHplNbMsUF257J0Xj2qk5Y+7fLcf9lrRAe6IeDZ4vwyFc7cPnrq/DFxuMor1IwcVMptqQ56wzRAWtB1O5KbcZDPocFkdoMBvtaok1zRa+yltRql5OFJwB9rMlnKz2YJdrwrthMrUVfe/tDQxGTKoqiiBbih/eCCaIArItjuMSgR1QfYmOVfjIfO7Ly4G8y4MbeSbavD28XD5PRgP1nCpF1oUSZFwsIAWLbieOGOkskSfaCqHMDbZeT9ZwiWoZP7wBObLn0fjl2uyGFKsj7DyX101dynn8wkNhbHNe3jmj3N9a9h7qLYIhGalOmKHr7poh/n7Fhgfi/se2x9qnL8cTotogO8cfxCyV4+n+7MeyVlZi39ihKKqq0HHJ1ji1zucdEy5zBKAIViJzAgsgbutwgepMLTgJ7f9RuHPkngJNbARiAdiouMhz0sEj5ObkVOPSH688vzQU2zxPHQx7X1y9apcSkivY5W1E0vu6iqFq4BHfbVstn1tmhKzo3Q1x4oO3rUSEB6J0sZlQVnSWyBSvsVO6cenI2XcyEmgIb/huT0CZiawAA2PTBpffbUuZ8qGWuvo1ZT+hkQ9aaOLbN1YV7D0GSJGyxFkR9UqsX7JHB/njw8jZY99Tl+Oe4DmgaEYgzBWV4fvFeDH55Bd5dcRgFZRpf6AXsBVHuMftFmJYDGm1iILmOBZE3+AXaZ002vKfdRoxyT23L/uouyA+Lt29Mu+IF17/fTR8CFUWi1VDpdU56EpMKTP0ZiEi0zxQVnbv0cQ0pXELH8ksr8cOOkwCA2wdc2pI4soP4P6NoQSSvIzq1Xblz6on8xqTNKCAoQtuxeIMcwb3nh0svcPhkypz1zWRZHmCuYTYgS0cbsl7MmQ1az+wGzuwScfBdGu/eQ1kXSnG2oBz+JgO6J0XV+JiQAD/cPSQNq/9vOF68pgtaxoTgQnEFXv39AAbNWo5Xf9+PnKJy7w7cUWgsEBQFQAI2vi++xnY5cgELIm/pfae4Snpyq73NwNvUbpdzNPBhICBMXPmWoy+dUV5k37dp8KMNc3bIUUyadaYoUSw8nz/+0qKooYVL6NR3W0+grNKC9gnhttkgRyM7ioJoY8YF5a6ItuwvbjNWAWUFypxTLxzb5RrSZqx1ad5dtPlaKoGt86vf54spc0FR9uOyvOr35Z8ACk+JkJfmPbw5Kuck9RNjyzsO5GWhrNKMv46crx4lvUPee+gK32plVNhm6+xQl8RIBPnXvV9PoJ8Jk/q1xPLHh+HNm7qjTXwYCsur8O6KIxj88gr8++e9OJ1f6o1hV2cw2GeJ5M2u27MgIuexIPKWsDixGB4Q62O8rfi8/UpZ+/Hqv15oE3sU7YpZgMXJdJptC0TLXEyaR2+iqswWfL0lC0fPF7t9Dq+JSbOuKbIWRY4zRQ01XEJnJEmytcvd1j8ZhhoK8dTYULSKC0WVRcKqA9nKvHCzbuKXeFUpsO8nZc6pF6d3igXtfkENv13Okfxzb8vH9jWjFrM9ZdSXCiKTn70oungdkXxhL6GzcvvZKSkwzN6SeuwvPPTldkz6cCPmrT0qvsa9h2zkgqhPivNFoZ/JiIk9EvH7I0Px/m290CUxEqWVZny87iiGvrICf/9+F47lePn3r1wQAUB8R30FfZDusSBSibmm3H55I819PwO5mV4dDw78KjYObNbNewllAx4EAsKBs7vt6XZ1qSoH/npbHA96xKOdpX/ZfRr/9+0ujHtrDX7Z5QMJXk1aiaIovLnYXV0uija8Zw2X6NPwwiV05K8jOcg4X4zQABMm9kis9XHyLJFibXMGA9D1JnG8c5Ey59QLW7vcaPHmtLHocBUQ1lQknMlFblm++PkL+N5MREgt0dsndLYha02SBwEATu1ahj+skfnvrTyC4vIq4NCfYtYuNF5EMzdi7hREMqPRgLGdE/DTg4Ow8M6+6Jsag0qzhC83ZWH4ayvxyKLtOHi2UOkh10xOmgO4GSu5jAWRCrZkXsDoN1Zh94mLNnFs2lG0PUkWYONc7w7Km+1yspAY+z5MzswS7fxSTHWHNwe63ezRS/91WPzyLqkw44EvtmHWkn363kMBEEXR1MXVi6KGHi6hE5+uF7ND1/ZsgbBAv1ofN8q6jmjF/nOoVOrfk1wQZa4B8rKUOafWGmO7nMwvAOhl3Y9t04fiVk6YC4r0vX1uatuc1ZYwp/+CyJyxxvalC8UVWLj+GPcessopKseRbDGT06uGVmFnGQwGDG0bh6/vG4Bvpg3AsLZxsEjADztOYfQbq3Hvwi3YdSJPoVHXwnGGiOuHyEUsiFQw/69MHMkuxmNf70BZ5UV5/QMeELfbFnpvzUBZPpCxUhx3uMo7rynrfz8QGCk2g937v9ofZ64C1r4pjgfOEEEUHpCveA1uLRYwf7AqA1M+2YQLxRUenVd1tqKomSiKKgqB+E5AmwYcLqGx0/ml+NM643Nb/7pnT3u0jEZMaAAKyqps/8Y8FpUEpAwRx7u/VuacWju1Hcg7JtImG3IwSm16TRWbXx9fD5ze5Zubsspqit6uLNPnhqwXa9kfEgxIkk6hXWgxnrtKxGp/s2obpIPcewgANlv3H2rbNAzRoQGKnLNPSgwW3NkXi2cMxhWdE2AwAH/sPYur3lmH2+dtVK+VrnkP0aIb21af69pI11gQqeDfV3dGbFggDp0rwuw/D1a/s9UI8Z+1ohDY/pl3BnToT7F5XpM2QFw777ymLDgKGPigOF75kuilr8neH4Dco2Jn9F5TPHrJ7MJyZJwvhsEAvDupJ96d1BMhASasO5yDCW+vvXTmTm+atAKm/iKKIgAY+jhg5H9VtXy5KQtmi4S+qTFolxBe52NNRgMub2/dpHVvDYmA7pJnRHcu0i6FUkl7vhe3bcfoc32J2iKaAR2vFseb5vp2QRRsbaNy3Jz19E4RHBEaJzab1qlzlUHYL4mLHDO75ePWfi2RGhuKoRWrYbBUNfq9hwDY4rZ7u9EuV5/OiZGYc1sv/PnoUFzbMxEmowFrDp3HvQu3oqJKhY6NiGbA9L/E7092VJCL+C5LBTGhAXj5ui4AgA/XZGDTUYdfJEajfaPWje/XXiAoSYt2OUf9pomFuecPAunfXXq/xQKseV0c97/f4zdQ8g/4dk3DERnij3Fdm+GHBwYhNTYUJ/NKcd37f+HbrSc8eg3VNWkF3LcauO17oFMD39BSQ5VmCxZtOg4AuL2e2SGZHL/9574z1ROrPNHhKnFl8/xB34/gliQROw00vnY5R3IE9+5vxF5MgG9FbstqWkNk23+or67feL70235sMIuLgANMB+BnMuKhEa1xvWk1AKC8s2et2Q3B5os2ZFVD6/hwzL6xO5Y/PgxNQgNw4Gwh3ll+SJ0Xa9JKbP1B5CIWRCoZ0aEpbuzdApIEPPHNTrGIU9b1ZiA4WrSUyHsDqaWyVMwQAdoVREERwKCHxPHKly7dz+LQ78C5vSKAoe/dHr/cphoWiLZtGo4fHhiEEe3jUVFlwRPf7MQzP6Src5VKKWHWxb46fsPh6/7cexbnCssRGxaIMZ0SnHrOkDaxCDAZkXWhFIfOFSkzkKAIe/qjr4crnNwK5GcB/qFA61Faj0Y7Sf2AhK5AVRmwwbovii/OENkKolz712zrh/TbLrf1WC6+33YSGy0dAAAG6watE+Jz0NmYiQrJhE+L9Tt+byipqEL6KdG63zvF/fVDzkpuEornJ3YGALy78gjST+q8W4MaFRZEKnpmfEckRgXj+IUSvPDrPvsdASFA77vEsbznjlqOrAAqi4GIFtr21Pa9V7wZuHCk+joJSQJWvyaO+9wlCkUPbbH2RNe04/aHk3vj0ZFtYTAAn244hls+3ICzBWUevyb5JjlM4Za+SQjwc+7HYWigHwa2Fm9sFd2kVW6bS//WHtfsi9Kt7XLtrhA/6xorg8E+S1R0Rtz6WsIccOkaIknSfcKc2SLh2Z/2AAASulwuvnhuD1ByAX67RdT2UksvvLPhAorKa9hwtpHYfjwPZouE5pFBaBHtnf+rV3ZphnFdmsFskfDENzv1fVGSGhUWRCoKD/LHqzd0BQB8sfE4Vh5wWHPQ9x6xO/bx9cDJbeoNwrFdTsuZhsBwYNDD4njVy/Y3fJlrgJNbRLuQHDjhgaLyKuw5Ja469anhipfRaMDDI9tg3pTeCA/yw9ZjuRj/9lpbmx01HofPFWJ9Rg6MBuCWvi1deq7cNrd0r4IFUdpwEQFckgMcXqrceb3JYhHrAYHG3S4n63J99Ys8oT7YMnfxGqL8EyIN1Oin24Xr32zJwu6T+QgP9MMD4/sDsda1s0dX2/YeWhs2GnkllVjwV6Z2A9WYLW471buF+nNXd0JMaAD2nynEuysOe/W1iWrDgkhlA1vFYurAFADA377bhfwSayEQngB0vk4cqzVLZK4U+w8B2rXLOepzt1iEm5spIrYB+9qhHrcr0ve77VguLBLQIjoYzSKDa33c5e2b4ucHB6Nd03BkF5bj5rkbsHB9pnJrQkj3Ptsg1g6N6NAUzaNq/7dSkxEdxL/V7Vl5yC4sV2ZAJj+gyw3i2Ffb5k5sBgpOAgFhQOuRWo9Ge/7BQM/J9s99smXuohkief1Q0866nAHML63EK78fAAA8PLIN4sIDgRQRv40VL9r2Huo/Svxf+3BNBgrLfHhG1gObVQxUqEtsWCD+fbUIs3h3xWHbRUxqGI5kF12asOwDWBB5wd/GtkdabCjOFpTjXz+l2++Q9+jZ8z8g/6TyL5y5FijLEwt5W/ZX/vyuCggFBj8qjle9ChxbL+LAjX72NUYecmWBaEpsKL6/fyDGdW2GKouEf/24B098s8sn/yOTa4rLq/CdNVjD2TAFR80ig9ElMRKSJPYkUozcNndgCVCap9x5vUXee6jdlYB/kLZj0YvedwEG669anyyILgpVyLK2y+l0/6E3/jyIC8UVaB0fhinWi5HyfkQ4LwoldL0R47onIy0utNHOElWaLdh+PA+AuoEKtRnXpRmu6JyAKouEJ77Zxda5BsJskXDPgi0Y9uoK9fedUhgLIi8IDjDh9Ru7wWgAftxxCr/uPi3uaNYNSB4MWKpENKvS5Ha59uMAo0n587uj951iF/f848BXt4qvdb0JiHKtZak2cqKfsy0AoYF+eOeWHvjHlR1gNADfbTuB69//CydySxQZD+nTjztOobC8CilNQmx7VbnKnjanYNtcQhcgviNgLre3nvkKx3a5zkxGtIlOFumZTdqIoAVfIxdxpXkiFdUxYU5nDpwpxKcbxLrAmRM6wt9kfYuTPLD6A7tPgslowMMj2gAAPlxztNHNEu09VYCSCjMig/3RJj7M669vMBjw76s7IzrEH/tOF+C9lWydA4CDZwvxzA/pOJVXqvVQ3LJ41ylknC9GeZUFaXHe/3flCRZEXtKjZTTuv6w1AOAf/9uNc4XWhfzyLNHW+UCFgpuVWSzA/sXi2NubsdbFPxgY8rg4LskBYAAGPaLIqcurzNiRlQegesJcfQwGA+4ZmobP7uqHmNAApJ8swIS312LtofOKjIv0RZIkfGZ903Rb/2QYje6trRvZUbTNrTmUrdysosFQfU8iX5K1QawtCYwAWl2u9Wj0ZcwLwIwtvhmqYFsDJQGFZ8RGs4DuEuYkScJzP++B2SJhTKemGNImzn5nRHMgOlUcO+w9NL5rc7SKC0V+aSXmr8v0+pi1ZGuXS452+2egp+LCA/Hc1SJ17p3lh7H3lJc2q9ep4zklmPThBny64RhmWkNBfInZIuHt5aKwvXtwKsIC/TQekWtYEHnRQyPaoGOzCOSWVOLp73eL9Sptx4of1GV5wI4vlHuxE5uBorPizUnqUOXOq4SeU4Dw5uK441VAXFtFTpt+sgDlVRbEhAagVZzrexkNbB2Ln2cMRtcWkcgtqcTkjzfi/VVHuK6ogdl2PA97Txcg0M+I63u1cPs8HZtFoHlkEMoqLVh3WMHiucsNAAwicOXCUeXOqza5Xa79OMAvUNuxkHJM/kBgpDg+sty6IWs8EOV6q6malqSfwV9HchDoZ8Q/x3W89AHtx4nbvvfYvmQyGvCQdZboo7VHUdCIZom0Wj90sQldm2FMp6bW1rmdqDQ3zta5nKJyTPlkE84XVQAQW0LofhP5i/y6+zQOnytCRJCfvV3Vh7Ag8qIAPyNm39QNASYjlu47h2+2nhCtbPJGrRvmiJkdJez7Sdy2HQP4BShzTqX4BwFXvw20GgGMfFax0zpe8TK4maiXGBWMr+8bgBt6tYBFAl5ash8PfLGtUUezNjTy7NBV3ZojKsT9/xsGgwEj5LS5fQquI4poDqRdJo53fV3nQ3XDYgb2/iiOuZFwwyPPbB38Tdwm6WtD1tIKM174RWxtcd+wVkiKqSHs4fJnxGbXPW6r9uXxXZujdXxYo5olkiTJtj1F31T19x+qi8FgwPMTOyMqxB97Txdgzsojmo5HCyUVVbhzwRYcPV+MxKhgXN5edB+8sfSgxiNznsUi4W3rZrt3DU5DeJC/xiNyHQsiL2ufEIFHR4kZkX//vFesVel+q7gCd+EIcOgPz19Ekhza5XSQLleT1iOB278HYtIUO+Vm6/qhvh5GiAb5m/DK9V3xn4md4W8y4NfdZ3DNu+uQka3QJpykmZyicvyyS6zhu32A51e4R3YUBdGyfWdhsSg4kyi3ze1aJP4/692xv8SMdFCkvZijhkMuiI6sELct9NUu9/6qIziZV4rmkUGYPqxVzQ/yDxLrdi9SbZZoTUajmCXKOF+MnOIKBPgZ0TkxUuvhID48CM9dJdoY315+CPtON57WuSqzBTO+2I6dWXmICvHHgjv74pnxHWEyGrB8/zlsP55b/0l04Pc9Z3DwbBHCg/wwdVCK1sNxCwsiDdw7NA29kqNRVF6F//t2Fyz+oUCvKeLODe96/gJn00W0tV9Qo4m+tVgkbDlm3ZBVgRYAg8GA2/onY9G9A9A0IhCHzhXh6nfW4U8l950hr/t6ywlUmC3o2iISXVtEeXy+/mkxCA0w4VxhOXYruet6+/GAfyhwIcO+Caae2drlJuhvRpo8JwcrVFrXueooYS7rQgneXyVmFf4xriOCA1wPEBrXpRnaxIehoKwKn6zNVHiE+iNfPOyeFIVAP30ELl3VrTlGdWyKSrOEJ79tHK1zkiThmR/TsWz/OQT6GTFvSm+0jg9DamworumRCAB4Y+khjUdZP4tFwn+XiXHeMSgVkcG+NzsEsCDShMlowOs3dEOwvwl/HcnBwvWZQL/7AINJbBx3ZrdnLyCny7UeKaKuG4GD5wqRX1qJkAATOjWPUOy8vZKj8fOMweiTEo3C8ircs3ALZv95UNnZAPIKs0XC5xvtYQpKCPQzYVg7sXh7qZJpc4Fh9tldvYcrmKvsLbrcjLVhCna4yKSzDVlf+GUfyqssGJDWBFd2SXDrHI6zRPPWZiC/tGHPEm3OlC8eatsu58hgMOCFiZ0RGeyP9JMF+GBVw2+d+++yQ/hyUxaMBuDtW3qgV7L9/9lDl7eByWjA6oPZut84/s99Z7H/TCHCAv1wp4/ODgEsiDSTEhuKp69sDwB46bf9yKiIAjpeLe7cMMezk8sFkV7b5VQg/4Dv2TIafiZl/1nHhwfh87v72zbYfWvZIdy1YLN9k13yCasOnsOJ3FJEBvtjQtfmip3XFr+t9Oyh3DaX/h1QpdDmr2o4thYozhZpZGnDtB4NqcFx/6SELiItVAfWHjqP3/acgclowMyrOrq9dhS4aJZonQ+FmbhBXm+rRDeFkuIjgvDsVSIQ47/LDuHAmUKNR6SeRZuO403r7M/zEztjdKfqxXzLJiG4wRr6o+e1RJIk4S3r7NDUgSkercvVGgsiDd3WPxlD2sSirNKCx77eiap+1nCF3d8AhW6+uTp/GDi3V1zFaztGucHqnNwC0FulK14BfkY8e1UnzL6xGwL9jFhxIBtXvbsW+880nl5nX/fpejE7dEOvFm611dRmeLt4GA3A/jOFyLqg4P5VqUOB8GYigVKJtYVq2f2NuO0wQSSSUcPjGBeuk/2HKs0WPPuziCa+vX8y2id41hlgNBrw8Eh5luhog50lOltQhuMXSmA0iA4IvZnYPREjO8Sj0ixS56oaYOvcsn1n8Y8f0gEAMy5vjVv71dyx8MDw1vA3GbDucA42ZOR4c4hOW7bvHPacKkBIgAl3DU7VejgeYUGkIYPBgFeu74rwID/syMrDB0dixC8bcwWw+SP3TrrfOjuUOtRh/4iGTZIk2xUvtXfcvrZnC3w3fSBaRAfjWE4Jbnh/PTLPK7h/FKki60IJVh7MBgDcqlC7nCw6NMAWXbtMybY5ownoeqM41mvb3IUM+9i63aLtWEg9jgWRTtYPLVx/DIfPFSEmNACPjlRm64YrOzdD26ZhKCyrwsdrG+Yskfy7sn1ChC6TwAwGA164pgsigvyw+2Q+PlidofWQFLX9eC4e+GIbzBYJ1/dqgcdG1f5vNykmBDf2TgIAzP7zoO62AJEkCW9Zk+UmD0hBdKjvzg4BLIg01ywy2Jau8ubSg8hqf4e4Y8s8oNKNnYobYbvcidxSnM4vg5/RgB4t1S8COydG4ucHB6N7UhQKy6ow7bOtKK1QaGNOUsXnG49DkoAhbWKRGqv8urpRasRvA0BXa9vcwd+BEh32ka94EbBUiQj95IFaj4bU4tgyp4OEuezCcrz5p2gjenJMO0SGKPPG3mg04OER4g3qx2uPNsi2aHvctr7a5Rw1jQjCzAnifdF/lx7CwbMNo3UuI7sIdy3YgrJKC4a1jcOsa7vU2+b5wPDWCDAZsenoBaw/oq9ZopUHsrHrRD6C/U24Z4hvzw4BLIh04ZoeiRhtTVe5b3MzSJFJQEmO63uQ5J8ATm4FYADajVNlrHokX/HqnBipaCtUXaJDA/D+bb0QGxaA/WcK8Y//7dbd1RsSyirN+HpLFgDRWqOGER3EvhEbj+YoG9vbtKNYs2GpFGuJ9OT0Lnu73MiZ2o6F1BXW1HqbAES11HYsAF79fT8Ky6vQOTHCdgVdKVd0TkC7puEoLK/CvAa4lmiTyu3lSrm2ZyIubx+PCrMFTzaA1rnsQrHx6oXiCnRtEYn3bu0JfyfWOzePCsYtffU3SyRJ9mS52wcko0mY72/GzYJIBwwGA168tguahAZg79kSrIi0JjVtmOPaHiT7fxG3LfsD4U2VH6hO2drlvHzFKyEyCG/f0hMmowHfbz+JzzYe9+rrk3OWpJ/GheIKNIsMsm14p7S0uDCkxYWi0ixhtbU1TzFyK9qur5Q9r6eW/Vvcdr6uxv1dqAFp0QcY/Cgw4b+ab8i6IysPX285AQB47qpOMBmVHY/jWqJPGtgsUUFZpW3dq94CFS5mMBjw4jVdEB7kh50n8vHhGt8tTovKq3DH/E3IulCK5CYh+HhqH4QG+jn9/PuHt0aAnxFbjuVizaHzKo7UeasPnceOrDwE+RtxzxDl9pPUEgsinYgNC8QL13QBADx6qCvMfqFA9j7gyHLnTyK3y7Ufr8II9cseIer9H/ADWjXB/41pBwD49897fGYTtcZEDlOY1Lel4gmEjmxtc0qnzXW+HjAYxX5E5w8re253Za4FDv8pwluG/0Pr0ZDajCZg5LNAu7GaDsNikTDzJxGkcG2PxGoxxUoa2ykB7ROss0RrG84alm3HcmGRgJYxIWgaEaT1cOqVEBmEf40XqXNv/HkQh3ywda7SbMH9n29D+skCNAkNwII7+iLWxdmUphFBuM0avKCHWSJJkvBfa/Ldrf2SERfu+7NDAAsiXRnbOQHX9khEvhSC/2G4+OKG95x7cvF54Ng6cdyh8RREF4orcPhcEQCgt0aJOfcOTcPYTgmoNEu4//NtyCnScURyI5N+Mh/bjufBz2jATX2Vba252MiOoiBavv+cspsKhjcVa3QAfcwSSRKw9Flx3HMK0KSVpsOhxuO7bSewMysPoQEmPHVFe9VeR6wlErNEH6/LRF5JhWqv5U1bNLx46K7re7XAZe3iUGG24Ilvd/lU65wkSfjbd7uw+mA2gv1NmDe1D1LcXMM67bI0BPkbsSMrDysPKNyF4KJ1h3Ow7XgeAv2MuG9ow5gdAlgQ6c7MqzqhWWQQ/ls8AhYYgMNLgewD9T/xwBJAsgAJXYHoFNXHqRdyu1yb+DDNEk4MBgNevaEr0mJDcTq/DA8v2gEzN27VBXkj1rGdExAfru4V0Z4toxEd4o+CsirbGw/FyHsS7VoEWDR+Q7D/FzFb5R8CDPs/bcdCjUZBWSVe/k38LnxoRBvEqzzDMcY6S1RUXoV5DSRxbpNt/yF9rx9yZDAYMOvaLggP9MPOrDyf+rt47Y8D+H7bSZiMBrx3a090T4py+1zx4UGYPCAFgLazRGLtkJgduqVvS9X/H3oTCyKdiQz2xyvXd0WW1BR/mHuLLzozS2RLl7tKvcHpkLz/UB+NE3PCg/zx/u29EOxvwtrD5zH7TyeKWFJVQVklfth+CoB6YQqOTEYDLm8vp80p3DbX7kogIBzIOw5kbVD23K6wmO1rh/pPB8IT6n48kULeWnoI54vKkRYbijsGqZ9oZTQa8Ii8lqgBzBKVV5mxMysPgPa/L13VLDIYz1hb517/86CtK0TPPl2fiXdXHAEAzLqmC4YrsH71vqFpCAkwYffJfOUTTZ20IeMCNmfmIsBkxLRhDas7gAWRDg1pE4fb+ydjXtUVAABp5yKguI64xbIC/H979x0eVZk9cPx7Zya9h4SQHpIQIECooVepIgp2EBULKva1rbqr67ruT1FZ21rBgm3BLjZAehcIHRICJCRAgEBCek/m/v64yVCkpEzLzPk8Tx5mJjP3vjDcmXvue95zyFyh3XaictuA1foPNUZCiA8zr9XWgb2zIoMl5l5LIprk+y1HqKipIyHE22oFN0Ynal96S9NyzXsFz9UTEidqt3fMM992m2rHfMhLB3d/GPiQ7cYhnMqBEyXMXZ8FwLNXJuJqsM6py5jEdnQO9aW0qpYPW/GiftDSh6tqjbTxciXWAq0HLO36PhEMTQimutbIE9/usOssjEW7j/OP+rVuj45O4IZk86Rrt/F247aBMYA2S2S0wb9Bw+zQjcmRtPNznNkhsFFAtGDBAmJjYzEYDPTr14+0tDQAdu/eTXJyMgEBATzxxBM2XzhmS0+P78TJgJ7sNLZHqa2ElI8v/OT9v2vNXNt0gOCO1hukjZVX17L7aH3FHDu54jWxR7jpA+vRr7dL01YbUVWVz//Q0uVu6R99yV4P5jKkQzCueh3Z+eVknDTzVcyGtLk9C5rXo6ylaiq1vkMAQx4DD3/rj0E4HVVVef7nVGqNKqM6t2VER8tUijyfM9cSfbLuIAVlrXeWaNNBLY23T0yA1T4PzUlRFGZe0w1vNwPbDhXabePclKxTPDx/G6qqpZQ9eFm8Wbd/15BYvN0MpB0r5vfU42bd9qVszMznj8xTuOgV7h3uWLNDYIOAKCMjg9tvv52ZM2eSk5NDdHQ006dPp6qqiiuvvJLevXuTkpJCamoqc+fOtfbw7Ianq4H/3NiDT+q0WaKqDR9A7QUW65/ZjLUVftA117ZDhdQZVcL83An397D1cEz+Nr4zvaKkaastbcjMJ+NkGZ6ueib1DLfafr3cDAyI05pYLkk1c0pD9CDwi4SqIm3NoLWlfATFR8A3HPreZf39C6f0e2oua/bn4arX8cwViVbf/9guISSG+lJWXceHrbjiXIpp/ZB9XDxsjjB/D565ojOgrc8x+0WnFjpwooQ7P02hqtbIqM4hvDCxi9mDzwAvV+4YFAPA60v2W3WW6K3lWt+h6/tEEmZH51zmYvWAKC0tjRdffJEbbriBkJAQ7r33XlJSUli4cCFFRUW89tprxMXF8eKLL/LRRx9Ze3h2pXd0IKEDb+K4GoBb5UlKtpynwlRNBexfot12snS5TXayfuhcrgYd7049o2nrj9K01dq+qJ8durpnOD7u5uli31gN1ebMvo5Ip4OkG7Tb1q42V1kEq2dpt4c/BS6O92Uo7E9lTR0v/JIKwPQh7ZtdoaslFOV0X6K567I41QpniYxGlZTs1ldh7nxuTI5kSIcgqmqN/PXbnXaTOpdbXMm0jzdTVFFDzyh//julp8XaPNw5OBYfdwPpuSX8tvuYRfZxrpSsU6w7kI9Bp3CfA84OgQ0CogkTJjBjxgzT/fT0dOLj49mxYwf9+/fH09MTgKSkJFJTUy+4naqqKoqLi8/6cUQPj01koYcW6BQsexP13ApTGSugpgx8IyCspw1GaDub7fiKVzs/d96a0hOdAt9vzeFLadpqNbnFlSzeowUjN1uhmMK5RnXWUnq2Hiogz9wl2JPq0+b2L4FSK5ZeXf82VJzS0nK732S9/QqnNmd1JkcKKmjn6879I8ybetQUYxLPmCVa0/pmifafKKWoogZPVz1dwnxtPZwWURSFmdcm4e1mYEt2AZ+ss33qXHFlDdM+3kROYQWxQV58NC0ZD1e9xfbn5+nC9MFaues3lu63SlD41nKtB951vSOICPC0+P5swaZFFaqrq5k1axb33XcfxcXFtG9/unKMoijo9XoKCs5fvvall17Cz8/P9BMZadkeI7biZtAz4IbHqVBdiao+wNplP579BCdNl6upM7LtUCGA1RbMN9XAuCD+Ok7rlfGvn1PZXl/hR1jWvE2HqDOqJMcE0DnU+l/+oX4edA33RVW1nkRmFZwAYb1ArYPd35l32xdSegI2vKPdHvks6BvfYd1cyqtrZZbVhgrLq7nhgw3cNOcPPt+QxYniSovvM6ewgndWaidhT4/vhJeb9f/fNVCU0xXnPl3f+maJGspt94zyt2hzamsJ9/fgb+O11LlXF6dz0IZrdatq65jx+Rb2Hi8hyNuNT+/oS6AVWoDcPjgGPw8XDpwo5ZedRy26r22HCli97yR6ncJ9w213YcLSbHpkPPPMM3h7e3P33XdjMBhwczu72627uzvl5eXnfe3TTz9NUVGR6efw4cPWGLJNdIqN5kCYNktUu+4djhXVL6iuq4H037TbTtSMFWDP0WIqaurw93QhPtjb1sO5oHuGxjK2SwjVdUbu+2JLq/sibW1q6ozM26TNxtlidqjBqM71aXOWqDTYUFzBWtXmVr+qzUKH9bJ6Wf+i8hr++u0OEv+xmJmL9lp13+K0rzYfZtPBU6zPyOfZBXvo99Iyrn9/PR+vPcjRQssU+HjxtzQqa4z0jQnkqu5hFtlHU4xODKFLmDZLNKeVzRI5wvqhc03pG8ngeC117olvbFN1zmhUeeKbnazPyMfLVc/c25OJDLTO7Imvuwt31zdFfXPpfos2rH1rmbZ26Jqe4US1cczZIbBhQLRkyRLef/99/ve//+Hi4kJgYCAnT56dAlJSUoKr6/kjbTc3N3x9fc/6cWSdJz0JwDB1C7PmLdSulmathcpC8AyCqAG2HaCVNfQf6hMdgE5nvzNjWtPW7rQP8uJoUSUPzdtmNznPjmhZWi65xVUEebsyrqvteuQ0BERr9udRWWPmohpdrwWdAY5thxMWDhJOHYSUT7Tbo/5ptVloVVX5decxRr62iq9TjgDw4ZqDZNrZImpnoKoqX23WLjhOSAqlZ5Q/qgqbswr41y+pDJy5nKvfXcfs1RkcPnX+C5hNtSEjn193HkOnwHNXJdpFVTRtligB0GaJ8s2dDmtBpn59DhQQNTRs9XLVk5JdwKf1Zdmtaeaivfy04ygGncL7t/Sma7ifVfc/bWAMAZ4uZOaVsWC7ZWaJdhwuZEW6Njtky7RVa7BJQJSZmcnUqVN57733SEzUqsYkJyfzxx+nGw5mZWVRVVVFYKDjHMAtYQjpSFn0SHSKSrcj8/hi46HT6XKdxoPOcvmq9mhTK7ri5evuwvs3n27a+vqSfbYeksNqKLV9Y3IkbgbbHRNdwnwJ9XOnoqaODRkX6SHWHF5BED9au71zvnm3fa4VL4KxBuIug9hhlt1XvWNFFdz12Rbu/99W8kqriG/rTe/oAOqMKv/5XY4da9t48BSZeWV4uep5+dokfrhvEOufuox/TEgkOSYARdEqfr74216GvLKCK/+7lndWHGh2GlNtnZHnf9Z6uNzUL4ouYdY9ybyYUZ3b0jXcl/LqOua0kr5EOYUVHC2qRK9T6Bnlb+vhmFVkoCdP16fOvbJ4r1XbXHy09iCzV2szha9cl8SQDsFW23cDbzcD99Q3R31r+X5qLDBL9N/6ynITe4TZpKiJNVk9IKqoqGDChAlMmjSJiRMnUlpaSmlpKUOGDKGoqIjPPvsMgJkzZzJq1Cj0euc60b8Yr2FaI8Qb9Kt499dN1Kb+ov3CymkstmY0qqdTAOx0/dC5OrY73bT17RUHLJNK5eQOnChl3YF8FEXr/2BLiqKYZomWmLvaHJxOm9v5DZxbaMVcju+CXd9ot0c+Z5l9nMFo1HpHjX5tNUvTcnHRaz1gfn1oMP93dVcUBX7ddYwdshbPqhpmh67qEWZaxxPm78Edg9vzzYyBbHx6JC9M7MKA2DboFNiVU8Sri9MZMWsl495YzZtL97M/t6TR+/ty4yH2Hi/B39OFx0bbV189RVH4y0htluizDa1jlqhhdqhrmC+errZbh2UpU/tFMTCuDZU1WtU5a5Sh/mXnUf79q1b068lxnbimV4TF93khtw6Ipo2XK9n55fywNces296dU8TStBPoFBx+dghsEBAtXryYtLQ05syZg4+Pj+knJyeH2bNnM2PGDEJCQvj222+ZOXOmtYdn39oPQw3pgqdSxXN8gKE8F6OrD7QfauuRWVXGyVIKymtwd9HR1Y6uHl7KxB7hTBugrWt55OvtZOdL01Zz+nKjNjs0slNbu6iCM7K+2tyytFzzf0knjAM3P60vUPZa8267wbJ/ASp0uQbCelhmH/UOnCjhhg828OyPuymtqqVnlD+/PjSER0Yn4GbQ06mdL1fX95N6edFeKbBgJUXlNfy2SyvrOzn5/BcZ2vq6c8uAGObd3Z/Nfx/FS9d0Y0iHIPQ6hb3HS3h96T5Gv76aUa+t4rXf00k7VnzB9+9UWTX/+T0dgMfGdCTACovTm2pk57Z0C/ejvLqO2a1gLZE9V2M1B0VRePnaJDxd9WzKOsVnG7LMst3qWiP5pVVk5ZWxO6eI9Rl5LN5znLnrDvLoVztQVZg2IJoZw2LNsr/m8nQ1mJqkvrV8P9W15rtA1rB26MruYcTZ8Vptc7H65YJJkyZd8MMwJiaG/fv3k5KSwsCBAwkOtv4UpF1TFJT+98GC+xmn3wzAouruBGSXMiDO7RIvdhymijmRAbgaWlfFnL9fkciunCK2Hipkxhdb+f7egRYtz+ksyqtr+XaLttbElsUUzjQgrg1ernpyi6vYfbSIpAh/823cxR26Xg1b5sKO+ea/KJK1Dvb/rq1VuuwZ8277DNW1Rt5flcHbyw9QXWfEy1XPX8d14ub+0ejPWRv46OgEftlxjPUZ+azZn8fQBPl+sLQfth2hqtZIp3Y+JEVc+uJTG283pvSNYkrfKArLq1mSmsvC3cdZs/8kB06U8tbyA7y1/AAxbTy5vFso47uG0jXc17RG6NXF6RRX1tI51JebbDzLeyENFefu/DSFz9Znc9eQWIK87ff7tyEg6uOgARGcTp179sfdvLwoncEdgvD3dKWkspaSyhrTn8WVtX96rOScx4rrb1ddIrC4vGs7/nGl+RuvNsfUftF8UF+i/tstR7ipX8uPndSjxfyemouiwIOXOf7sENggILqU8PBwwsOt11m+1el6HSz9J5RpBSh+qu7Dko828tyVidzSP9ouDk5LS8mqbzDXStLlzuRq0PHO1F5MeGstaceKeebH3cy6Pskp3jdL+mn7UUoqa4kK9GSoDXK5z8fNoGdoQjALdx9naWqueQMi0HoSbZkLqQtg/CxwNdOsmKpqnzEAvW6FNpZpwrclu4Cnv9/JvlytUMJlndry70ldL9gBPSLAk5v7R/PxuoO8vGgvg+OD7LqgSmunqirz69PlpvSNavJnlL+nK9f3ieT6PpEUV9awLC2XhbuOs3LfSbLyy3lvZQbvrcwgIsCDy7u2IzHMl/mbtQqR/7wy8U8BsT25rFNbkiL82HmkiDmrM03rWOxNQVm16fhKjgmw8Wgsa2rfKH7beYwNmfmMem212bbr6arHx92Aj7uL6c/OoT48MirBbv6PerjquW94HM//nMrby/dzbe/wFq+hbVg7dEW3UOLb+phjmHbP7gIicQku7pA8HVa+hGpwxydhLHU7C/jHgj2kHSvm+au6trpZk6baZKqY0zo/4EP9PPjvlJ7c/NFGvtt6hF7R/kztZx+zGq2RqqqmYgo394+yq5PkUZ1DWLj7OEvSTvDoGDOvh4jqD/7RUJgNe3+FpOvNs930hXBkExg8YOhfzbPNM5RW1TJrcTqfbshCVaGNlyv/vKoLE5JCL3nS/cBl8Xydcpg9R4v5ZdcxuyjH3BjVtUZKq2qt0p/EXHYcKWLv8RLcDDom9WjZRUpfdxeu7hnB1T0jKK2qZcXeEyzafZzle09wpKDirAIFV3UPo19sm5YO36IaZonumJvCZxuyuWuofc4SbcnWLh7GBXvRxg7HZ046ncIr1yVx1dtrKSivAbSiA1oQc3ZA0/CY7xm3fdzO/r2vuwtebvpW07dpSt8o3l+VwdGiSr7efJhbBsQ0e1vpx0tYuPs4AA+N7GCmEdo/CYhao753w6ENKLHDeWXQAOLDM5m5aC/zNh1mf24p793cm2Afx/zwO1pYQU5hBXqdQq+o1hkQAQyMD+KJsZ14edFenv8plS5hfvSI9Lf1sFql7YcL2XO0GFeDjut721eD5hGd2qJTIO1YMUcKys27tklRtOIKq17Wqs2ZIyAy1tWvHQL6zwDf0JZv8wzL9+byzA+7OVqkNfa8rncEfx/fudFrRQK9XLl7aCyvLdnHf35PZ1yXdnZ/AajOqHLLRxvZdriQH+4baFdV0y5mfn0/r/HdQvHzdDHbdr3dDFzZPYwru4dRUV3Hqn0nWLj7OMvSTuDuouPp8Z3Mti9LGtGxLd0j/NhxpIjZqzNNjULtiaOvHzpXZKAnf/xtJJU1RrzdDHYzg2MN7i56HhgRz7ML9vD2igNc3ycSd5fmzRK9VT87NL5bOxJCnGN2CGzcmFU0k2cg3LoABj+CoijcMyyOj29LxsfdQEp2AVe9vZZdR4psPUqLaPiA7xLma9PO5eYwY1gsYxKlaWtLNcwOXZkUZneLsAO9XOkTrZ2MLEs7Yf4dJN2o/ZmxHEqOt3x7O7+Ck2ng7g+D/tLy7dXLK63ioXnbuGNuCkeLKokM9OCLO/sx6/ruTX7P7hzcniBvN7Lzy/mqPsXKns1dn8XGg6eorjXy7soMWw+nUUqravlph9bXZHKy5S4yeLjqGdc1lDcn92Trs6NZ99RlhPqdP2XS3pzZl+izDVmcLLG/inPOFhCBlqrs5+HiVMFQgxuSIwnzcye3uMrUoLyp9ueWmAqpPHiZ88wOgQREDmNEx7b8eP8gYoO9OFZUyXXvr2fBdvOWYLQHmxyowZyiKMy6QZq2tsSpsmp+2al9eN/c3z4XYY9K1KrNLbVE+e02cRDRF1Qj7Pq2ZduqrdL6DgEMfgQ8/Fs8PFVV+XbLEUa9toqfdhxFp8DdQ2P5/S/DGNwhqFnb9HIz8NBIbZHvm8sOUFZV2+JxWsqh/HJmLU433V+46xiH8s3TvNSSftlxlPLqOmKDvOhrpbWargadTXuHNcfwjsF0j/SnssbI7NX2FexW1tSxK0e7MOoI35fi0twMeh6oD2LeXZlBRXXTm4K/veIAqgpju4TQOdTX3EO0axIQOZC4YG9+vH8QIzoGU1Vr5OH525m5cK9DnWSbCio4yAe8r7sL793cC3cXnTRtbaLy6lo+WXeQ6lojXcN97TblsKEf0R+Z+ZRU1ph/Bw09iXa0sEnr5o+g6DD4hEK/e1o8rEP55dzy0SYe/2YHheU1JIb6suD+wfxtfOcWV1acnBxFVKAneaVVfLzWPhtkqqrK0z/spKKmjv6xgQzpEIRRhY/W2n+p5oZiCjcmR0rBl4toWEsE2kz1iZJKG4/otO2HC6mpUwnxdSMysHXMuomWu653BBEBHpwsqTK1omisjJOl/Fw/M+xss0MgAZHD8XV34cNpyaa69O+vyuDOTzdTVGGBEzErKyyvJr2+wV+fVlpQ4Xw6tfNl5jVJgDRtPZ/KmjpSjxazYHsOryzay/RPUxj6ygq6PLeY/y4/AGDXFRZjg72JDfKipk5l9b488++gy9Wgd4XcXXB8d/O2UVkMa2Zpt4c/BS7NP4GqrTMyZ3UmY95YxdoDebgZdDw5rhMLHhhEt0aUbm4MV4OOx8Zo6UofrM60y3TTr1MOs+5APu4uOmZek8SM+o7yX6UctsvxNth7vJjthwsx6BSu7W27hpOtxfCEYHo0zBKtsp9gt6Eha5+YQLv9bBTm52rQ8VB9MPPeygzKqxs/g/7O8gMYVe0iXtfw1rHW0ZwkIHJAep3Ck+M68ebkHri76FiZfpKr31lHxslSWw+tRRpmh2KDveyyok9LTOoZzq1O3rS1ps7I/twSftl5lNeW7GPG51u47D8r6fLcYsa/tYaH52/n3ZUZLE3L5dCpclOFsvHd2jGxhVWwLG1UojZLZJG0Oc9ASBir3d7ZzFmiDW9DeT60iYceNzd7KHuOFnH1u+v5v9/SqKwxMiC2DYv/MpR7h8fhYuZqTVcmhdElzJfSqlreWXHArNtuqdziSv79axoAj43uSEyQFwPj2tAlzJfKGiOfb2jalVtrmr9Jmx0anRjicJ+zlnDmLNEXG+1nlmhzfYW5vg6STSEa7+pe4US38SS/rJrPGvlZczCvjB/rl1k0pCQ7m9a9Kl1c1MQe4cQFe3P3Zylk5pUx6e11vDWlJyM6tbX10JqlYYGoo37AP1PftHWbgzdtrTOqHDpVTvrxEvbnlpCeW8L+3FIy80qpqTt/eqevu4GO7XzoEOJDxxAfOoR4kxDi02pO2EZ1DmH26kyW7z1BbZ3R/KVckyZD2s+w8xsY9TzomvD/pvQkrH9bu33Zs6Bv+tdCZU0dbyzdz5w1mdQZVXzdDTxzRSLX94mw2NVpXf2Fn1s/3sTnG7K5fVCMeav4NZOqqjzz425KKmvpHunPHYPbA9qJ891DY3l4/nY+25DFPcNim10FylIqa+r4fqvW4HiynTZGtUfDEoLpGeXPtkOFvL8yk39cmWjT8dQZVbbWB0SOlE0hGsdFr80SPfbNDj5YlcHN/aPxvkQRqndWaLNDIzoGm79nXishAZGD6xrux4IHBnPfl1vYnFXAHZ9u5q9jOzFjWGyrm0bf5OAVc1wNOt51sKatJ0uq2Hmk0BT07Mst4cCJ0gt2Afdy1f8p6OnYzoe2Pm6t+t+hV5Q/AZ4uFJTXkJJdQH9z91npMAY8AqD0OGSuhPiRjX/t6lehpgzCekLixCbvOvVoMfd9uYWs+mIBV3QL5bmrEmnr497kbTXVkA5BDIxrw/qMfF5fsp//3NDd4vu8lF93HWNJai4ueoVXrk06q9rVFd1CeWVROjmFWkf5m/vbV/+xRbuPU1xZS7i/B0Pim1f0whkpisIjoxK49eNNfLExm7uGtrdptby0Y8WUVtXi42agUzvnWhgvNBN7hPHOigNk5pXx6fos7h9x4Vmf7PwyftjWMDvkfGuHGkjKnBMI9nHjy+n9mdI3ClWFlxft5eH525tVgcRWKqrrTKXErVX1yBYamrbqFPhu6xH+18zSmbZWWVPHa7+nM3DmMu78NIVXFqXzw7Yc9hwtpqrWiLuLjq7hvlzTK5ynLu/EJ7cls/bJEex+fiw/3j+Il69LYvqQWIYmBBPi696qgyEAg15nmpm1yBoxgyt0vVa7vfOrxr+uIAtSPtZuj/qn1tuoiZ75cRdZ+eWE+Lox+5bevDO1l1WCIdBORJ8cp/Wt+X7bEdKPl1hlvxdSUFbNcwv2AHDf8Hg6tju7h4dBr2P6EG3G6MP62TR70lCq94Y+kXbV4Lg1GNIhiL7tA6muNfLWMtumcDZkU/SKDnDK8tNC+6x5uD6Vc/bqTIovUtDn3RUZ1BlVhiYE07MV93dsKQmInISrQcdL13Tj35O6YtAp/LTjKNd/sJ6jhRW2HlqjbD9cSK1Rq5gTEeDYFXMGxgfx+NiOADz/Uyrvr8qgqrb1BK/rM/K4/M01vLX8ADV1KvFtvbmqexiPj0lg9i29Wfn4cPY8P45fHhzCazf0YMawOEZ0aktEgGerD3wuZnTn0+uIVNUCJ8Ldp2h/pv0MVY1cL7jiJTDWQOxw7aeJjhVVsPVQIYoCP94/iDFd2jV5Gy3VPdKf8d3aoarw6uK9Vt//mV74JZX8smoSQry5b0TceZ9zQ59I/DxcyMovZ0mqGXpHmUnmyVI2HjyFToEbkqWYQlMpisIT9Z/bX6ccJivPdutAG9bbOvLFQ3FpE5LCiG/rTVFFDZ+szTrvcw6fKue7+jTZh514dggkIHI6N/eP5ovp/Qj0cmV3TjFXvb3WdDXJnp3ZYM6RT5ob3Dssjiu6hVJdZ2Tmwr2MfX01S1MtdCJtJgVl1Tz+zQ5umrORg3lltPVx492pvVjyyFDemtKTBy7rwJgu7YgJ8nLKq5ZDEoJx1evIyi8n46QFTpbCe0NgHNSUa0HRpRzffXo2aeRzzdrlot3aCX2f6ACbpgg9PqYjep3C0rQTNvs8W5F+gu+35aBT4OVrky7YU8fLzcAt9aly76/KtJtj+qsUrZjC8I5tW01zVHuTHBPIiI7B1BlVXl9qmxYKqqo6fHq5aBy97nTBjw/XZlJU/udZondXZlBrVBkcH0TvaOedHQIJiJxS/9g2/PTAIDqH+pJXWs1Nc/5odldjazEVVHCSK16KovDfKT159bokgrzdyMovZ/pnKUz7ZDMHTtg2Lehcqqry/dYjjHxtFd9uOYKiaE1Slz42jPHdQp0igG0MbzcDA+K0tUMWqTanKKdniXbMu/Tzl78AqJA4CcJ7NWuXC3dpAdHlXUOb9XpziQ325oY+kQDMXLjX6kFGaVUtf/9+FwB3DGp/ybSTaQNjcDXo2H64kM31V/NtqbrWyHdbtKvENyZH2ng0rdtjY7RZop92HCXtWLHV95+dX87Jkipc9TqSzFTmXrRe47uG0jHEh5LK2j/1QNPWMmoXQpx57VADCYicVESAJ9/dO4ArkkKpqVN5+vtd/GPBbmrqzr/Y3ZZq64ymijnOdMVLp1O4vk8kKx4fxj3DYnHRK6zed5Jxb6zhXz+n2kVvqay8Mm7+aCOPfr2DU2XVdAzx4dsZA/n3pG74urvYenh2Z1RnC64jAki6Qfvz4Gooyrnw87I3wL5FoOi1ynLNcKKkks3Z2oWKcV2tnyp3rr+M6oC7i44t2QUsTTth1X2/vHAvR4sqiQr05NH6/kgXE+zjxrW9tLS02aszLD28S1q+N5e80mqCfdy4rJVWIbUXXcP9uKJbKKoK//nd+rNEDRcPkyL87K6KobA+nU7hkdFasPPxuiwKzuiB9t5KLa19QGwbp7nYfDESEDkxT1cDb0/pyRNjO6Io8NmGbG7+cCP5pVW2HtpZUo8VU1Zdp5VeDvG59AscjI+7C09f3pnfHxnGqM5tqTWqfLzuIJfNWsm8TYdssjC7utbI28v3M+aN1aw7kI+bQccTYzvyy0ODnX7a/WJG1q8j2nKowDLHWUA0RA8CVNj19fmfo6qw9J/a7V63QFDzek4s3pOLqkKPSH/C/G2fYhXi687tg7SCBa8u3mu142LTwVN8/ofW62PmNd3wdG1c8da7hrRHUWBp2gmbz/rOq+89dF3vCLP3i3JGj4xOQKdoM8HbDll3BrAhIOrjRBcPxcWNSWxHYqjWs23OGm2W6FhRBV9v1maFZXZII598Tk5RFO4fEc+cW/rg7WZg48FTXPX2OlKPWn+q/0IaUkr6xAQ6deWj9kFefDgtmU/v6EtcsBf5ZdU8/f0urnp7LZsOWm/dRErWKSb8dw2zft9Hda2RwfFB/P7IUO4fES8nU5cQ5u9BlzBfVBWW77XQLEbSjdqfO+Zrwc+59i2Cw3+AwR2GPdns3SzcdQyA8d1sPzvUYMawOPw8XNiXW2rqp2NJlTV1PPndTgAmJ0cysAmlqmODvU2FNuasPmiR8TXGkYJyVu8/CWh/B9Fy8W29TTOAs35Pt+q+TxdUkAtTQqPNEmkz13PXZ5FfWsUHqzKprjPSt32gKZXb2cnZiwBgVGIIP94/kJg2nuQUVnDte+v5rf6Ex9Y2H2y44iUf8KA1AVz0l6E8OyERH3cDe44Wc8MHG3hw3jaLVg0sqqjhbz/s4rr3N7Avt5RAL1dev7E7n9/Zl+g2Xhbbr6MZdUa1OYvoMgn0bnByLxzbcfbvjHWw7F/a7X4zwDesWbvIL63ij8x8wPbrh87k5+HCfcO16m6vL9lHZY1lqzO+uWw/B/PKCPF14+nxnZv8+nuGxQLww7YcThRXmnt4jfJNyhFUFQbGtZHj2IweHtUBF73CugP5rDuQZ5V9niypIjOvDEWB3lEyQyROG9W5LUkRfpRX1/F/v6aZWno4e2W5M0lAJEzi2/qw4P7BDOkQREVNHQ/8b6vNZ4pUVT1dUEFSAExc9DruHNyelY8PZ0rfSBQFft5xlMv+s5I3l+4364mgqqr8svMoo15bxf82NvQpiWDZo8O4umeEFE1ootGJWkC0el+eZU7Y3f2g03jt9rk9iXZ9AydStecM/kuzd/F7ai5GFbqG+xIZ6Nn8sVrAtIExhPq5c7Soki/qU9ksYXdOEbNXa+kn/57UDT+Ppq+Z6x0dSO/oAKrrjHyyPsvMI7y0OqPKN/XV5aSYgnlFBHgytZ9WTfDVxelWKfSxpX5NX8cQH/w8ZQ2nOE1RTs8Sfb8th+paI72jAxgos0MmEhCJs/h5uvDJbcmMTgzBqMIrNu7rkZlXRn5ZNa4GHd2kYs6ftPF246Vrkvj5gcEkxwRQWWPk9aX7GPmfVfy261iLv4QPnyrnjrmbeeB/2zhZUkVssBfz7+7PK9d1J8DL1Ux/C+fSJcyXdr7uVNTUsaF+lsXsGqrN7foG6mq127VVsPz/tNuDHwGP5s+4LtxtH9XlzsfdRW8qNfv2igMXbUjYXDV1Rv767U7qjCoTkkJNQW5z3DNUmyX64o9sSqtqzTXERlm97yRHiyrx93RhrA16SDm6+0bE4eGiZ/vhQqsU+th0sCG9XLIpxJ8NTwimZ5S/6f7DIzvIBc0zSEAk/sSg1/HMFZ0x6BRWpp9kfYZ1pvvPpyFdrkek/wX7egitstHX9wzgv1N6EubnTk5hBfd9uZUpc/5oVunX2jojc1ZnMub11axIP4mrXsfDIzuw8OEh9I+VK0otoSgKoxItXG0u7jLwDIKyk5CxXHss5RMoOgQ+odD3nmZvurC8mvX1KUCX20F1ufO5tlcEccFeFJbXMHtV5qVf0ESzV2eSeqwYf08X/nlVlxZta1TnEGKDvSiprGW+ldsfzN+s7e+anhFSkcwC2vq4c/ugGABmLU7HaOFCHynZ0n9IXFhD82CdorUwGdKh8WsenYEEROK8ott4MbVfFKCVlLVV88BNki7XaIqicGX3MJY9NpyHRnbAzaDjj8xTXPHWGv7+wy5OnVFu82J2Hilk4jvr+L/f0qioqaNv+0B+e3gIj4xOkKDUTM5cR2SRY0vvAt2u127vmAdVJbD6Ve3+sCfBtflpbktSc6k1qnRq50NssLcZBmt+Br2Ov47rBMBHaw+adX3OgROlvLl0PwDPXZlIkLdbi7an0yncNUSbJfp47UGrtT44UVLJsvpZi8l9JV3OUu4ZGoePu4H03BJ+3nnUYvspq6plT32KuwRE4kIGxgWx/LHhfHJbsswOnUMCInFBD1zWAU9XPTuOFJlSZKwtJUtSAJrKw1XPo6MTWPbYMK7oFopRhS83HmL4qyuYu+7CJ1ylVbU8//MeJr2zjj1Hi/HzcOHla7sx/67+xLe1zxPf1qp/bBs8XfXkFlexO8dC6/S611ebS/8NVrwE5XkQGAc9b27RZu05Xe5MYxJD6BXlT0VNHW8t32+WbRqNKk9+t5PqOiMjOgYzqUe4WbZ7dc9wgrzdOFpUyS8WPGk+07dbjlBrVOkV5U+CE7YzsBY/TxdmDNMKfby2ZJ/FAt5thwqpM6qE+3vYRRl8Yb9igrzwcmtcewBnIgGRuKBgHzfTlctZi9Ot3rQ1t7iSQ6fK0SlIb5tmiAjw5J2pvZh/d386h/pSXFnLP39OZfyba1i7/+w0yCWpuYx+bRWfrMvCqMLEHmEse2wYNyZHOXWpc0txd9EztEMwAEssVW0utAcEd4LaSvjjHe2xkc9qs0fNVFxZY/q/Y0/lts9HURSerJ8lmr/pMFl5ZS3e5mcbstiSXYCXq57/u7qb2a6wurvouW2gtgD/g1WZFp+RV1WVrzZrxRQm942y6L4E3DYwhiBvV7Lzy/l2i2XKwZuyKaTBphDNIgGRuKi7hsbSxsuVzLwyvq6vRmQtDb11Oof64uMuFXOaq39sG355cDD/ntSVAE8X9p8o5eaPNnL3ZylsyT7FjM+3cNdnKRwrqiQy0INP7+jLm5N7tjgVSFzcqPqF+BZbR6Qop3sSgRYgdZ7Yok0uTztBdZ2R+LbedGgFswr9YtswomMwtUa1xf1gDp8q55XF2jaeGt/Z7Ffhb+4fjaernr3HS1iz37LrNjdk5pOdX463m4EJSfY90+cIvNwM3Ddca4Bs7iqgDVKypD2FEC0hAZG4KG83g6mL8RtL91Nebb0qSA3ltiUfuuX0OoWb+0ez8vER3DYwBr1O4ffUXK59bwOL9hxHr1OYMSyO3/8yjGEJwbYerlMY0TEYnQKpx4rJsVT/qKQbgPpZjFHPga5lH/kNvcnG22kxhfP567hOKAr8svMYu44UNWsbqqrytx92UV6trambaoFZFX9PV1Pp64Zy3pYyf5N2ceuqHmF4ukrqjDXc1C+KMD93jhebvxx8TZ2RbYcKAVlvK0RzSUAkLmlK3yiiAj05WVLFJ+uyrLbfhhkiSQEwH7/6qlgLHx7C4HitwkyPSH9+eXAwT13eCQ9XKZpgLW283UypoMstlTbnFwHXfghXvKZVnmuBsqpaVu07CcA4O18/dKbOob6mtT4vL2peG4HvtuawZn8ebgYdM6/pZrE00jsHt0evU1h7II/dOc0L3i6loKyaRfXrwKYkS7qctbi76Hm4vhz8uyszzFpifXdOERU1dfh7uhBnp4VOhLB3EhCJS3I16HhsjNbQ6/2VGY2uVtYSRRU1pOeWAJICYAkJIT58fmdf1j11Gd/fO5DOob62HpJTaqg2Z9GiJd2ug+Q7W7yZFeknqKo1EtPGk86h9p8ud6ZHRyfgotcCjXPXz13KiZJKXvglFYBHRidYtLJeRIAnV3TTgk1LzRL9sC2H6jojiaG+dA2X496aru0VQfsgL06VVfPx2oNm266p+FB0oKz5FKKZJCASjXJlUhhdwnwpqarlnRUHLL6/rdkFqCrEtPGkrY+7xffnjBRFIdzfQ75AbWh8t1AUBdZn5JNxstTWw7mohbvqq8t1C2115VojAz2Z2k8rWvDyor1N6gfz3II9FFXU0DXcl+mD21tqiCZ31zdq/XXXMY4UlJt122cWU5jSN7LVvY+tnUGv49HR2sXFOaszKTDTxcVNpvRyuXgoRHNJQCQaRac7XbHp8w3ZZv+iPtcmWT8knEBkoCcjO2mzRJ+tz7LtYC6iorqO5Xu1njXjW1G63JkeuCweL1c9u3KK+G33sUa9ZuGuYyzcfRyDTuGVa7tj0Fv+K7NruB+D44OoM6p8ZMZZBIBthwtJzy3B3UXHVWYqGS6a5opuoXQO1S4uvr86o8XbU1XVVFAhWdLLhWg2CYhEow3pEMSg+DZU1xl5bck+i+5r80H5gBfO4baBMYDWF6akssa2g7mAVftOUFFTR0SAR6tNswryduOuoY1vI1BUXsOzC/YAMGNYHIlh1vt7N8wSfbX5MEXl5vs/MX/TIUCbmfTzkMqdtqDTKTwxVpsl+nR9FrktbBqccbKUgvIa3F10dA3zM8cQhXBKEhCJRjuzr8cP23JIO2aZhpKVNXXsrK8GJRVzhKMbFN+G+LbelFXXWaxHSUudbsbarlWnWU0forURyMovN6WOXci/f00lr7SKuGAvHhwZb6URaoZ0CKJzqC/l1XV8sdE8FclKKmv4eYc2MzZFeg/Z1IiObekV5U9ljZG3l7csBX1z/fqhHpH+uBrklE6I5pKjRzRJUoQ/E5JCUVV4pZkVmy5l55EiquuMBPu4Ed3G0yL7EMJeKIrCtPpZos82ZDdpfYs1VNbUsSxNS5e7vFvrTJdr4O1m4MHL6vvBLLtwG4E1+0/yzZYjKAq8cl0SbgbrVl9UFIW7h2rrlT5Zl2WWvjU/7zhGRU0dccFe9JFG1zalKApPjK1vGrz5EIdPNT8F3ZRNIRcPhWgRCYhEkz0+piMGncKK9JP8kZlv9u1vPmOBaGu+Gi1EY13TMxwfdwMH88pYvf+krYdzlrX78yitqiXUz50eEf62Hk6L3dQvmshAjwu2ESirquWp73YBMG1ADL2jbXOiOSEpjDA/d/JKq/hhW06Ltzd/s5YuNzk5Sj5X7cCAuDYM6RBETZ3KG0v3N3s7m7MlIBLCHCQgEk0WE+RlSrmYuXAvqmreK9qb5IqXcDJebgau76015ZxrZ8UVGgoQjO3SziEqEroadDw+piOgtRE4t9LXq4vTySmsICLAgyfGdrTFEAFw0eu4o76q3Zw1mS2aOdxztIidR4pw0Stc00uKKdiLhv+HP2w7wv76NhNNcbyoksOnKtAp0Etm/YRoEQmIRLM8ODIeT1c92w8XsniP+Xqo1BlVtmZrOdESEAlncuuAaBQFVqaf5GBema2HA0B1rZGlqVrT2PGtPF3uTFcmhZFYX+nr3ZWn13BsyT7FpxuyAHjpmm54uRlsNELN5L5R+LgbyDxZxtIWNO9tWC81JrEdbbzdzDU80ULdI/0Z2yUEo0qzChU1ZFMkhvnibeP/q0K0dhIQiWZp6+PO9CFaJaRXFqVTe4mKTY2VdqyYkqpafNwM0ixUOJWYIC9GdGwLaNWn7MH6jDyKK2sJ9nGjtwNdgdbpFP46Trs6/+mGbHIKK6isqeOv3+5EVeH63hEM6RBs41Fqa55u7q/1T2puo9aK6jpTyt3kvpFmG5swj8fGdERRtMIlO48UNum1m6U9hRBmIwGRaLa7hrQn0MuVzLwyvk4xT3Wshg/4XtEB6B0gPUeIpjizBHdp1fkX/FtTQzPWcV3aOdzxOCwhmP6xgVTXGnljyT7eXn6AjJNlBPu48cwVibYensntA2Nw1etIyS5gS/16kaZYuPsYJZW1RAR4MCguyAIjFC2REOLD1fU9oWb93rRZIkkvF8J8JCASzebj7mKq2PTG0n1UVLe8ElJKVkO6nONcjRaisQbHBxEb7EVpVS3fb7VtCe7aOiO/p54ut+1ozmwj8N3WI7y/SmuS+cLELvh52k+Pnra+7kzqGQbAB6uaPks0f5OWLndjn0iHWAPmiP4yKgGDTmH1vsYXKiqqqCG9ft1RH/m+FKLFJCASLXJTvygiAz04UVLFx+ta1lVdVVU2SQqAcGI6ncK0ATGAVlzBliW4Nx48RUF5DYFervR10AbJPaMCGNelHUYVao0ql3dtx7iu9rdWqqFR65K0XDJPljb6dQdOlLIp6xQ6Ba7vI+ly9iqqjacpnXHW4vRGFSraml2AqkJMG0/a+rhbeohCODwJiESLuBn0PDb6whWbmiI7v5yTJVW46nV0j/Q30wiFaF2u7R2Bt5u2kH7tgTybjeO3XQ3V5UIw6B33q+LxsR1x1esI8HTh+YldbD2c84pv68Oozm1RVZizpvEXnr5O0WaHLuvUlnZ+ctJszx68rANuBi01cuW+S5fel/VDQpiX437LCau5qnsYnc9TsampGmaHkiL8cHexbiNEIeyFt5uB63pHALYrwV1nVE3VIy+3wxkTc4pv683Cvwzh14eG2PWV9ruHxgFaet/JkqpLPr+61sh3W7S0yxuToyw6NtFyIb7upgbNsxanX3J2WAIiIcxLAiLRYjqdwlOXa7n4n67P5khB87pumzpuO2h6jhCN1XBitCL9BNn51i/BvTnrFHml1fh5uDAgro3V929tccHehPl72HoYF5UcE0CPSH+qa42NqkK4JDWX/LJq2vq4MaKj7SvmiUubMSwObzcDe44Ws3D3hdtZVNbUseNwESDfl0KYiwREwiyGdghiYFwbquuMvL6keV23U7KloIIQAO2DvBjeMRhVhc82ZFt9/4vqT8ZGJ4bg4sDpcq2JoijcU7+W6PM/sim7RBXC+ZsPAXB9nwiHTnl0JIFerkwfojXj/c+SC7ez2JVTRHWdkSBvV2LaeFpziEI4LPmUFGZxZsWm77cdYe/x4ia9/kRJJQfzylAU6B0tV7yEaJgl+nrz4Uue/JqT0aiycLe2fmh8N8erLteajenSjpg2nhRV1JjWB53P4VPlpvVnN/aRdLnW5M7B7QnwdCHzZBnf1/ePOteZ6XKKIpUDhTAHCYiE2XSP9OeKbqGoKry6KL1Jr20ot90xxAc/D/speSuErQzrEEz7IC9KqmoveGJkCdsOF5BbXIWPm4FB8dK3xp7odYqpIfZHaw9ecAbh65TDqKpWxj1KZhBaFR93F+4brrWzeHPpfqpq/9zOYrP0HxLC7CQgEmb1+NiO6HUKy/aeYGMj+ynA6QZzjlreV4im0ukUbh0QDcCn67MaVYrXHH6rb8Y6snNb3AxS3MTeXNc7gjZerhwpqOC386wzqa0z8k1KQzEFKbXdGt0yIJoQXzdyCiuYt/HQWb8zGtUz0svl+1IIc5GASJhV+yAvptT3U5i5aG+jT+KkYo4Qf3Zd7wi8XPUcOFHKugONv8DQXKqqmtYPXd7NsavLtVbuLnpure9V9cGqjD99xq7ad5LjxZUEeLowpkuIDUYoWsrdRc9DIzsA8PaKA5RXn06ZTc8toaSyFi9XPZ1DfWw1RCEcjgREwuweGtkBDxc92w4VsnhP7iWfX1JZQ9oxbc2RBERCnObj7mLVEtw7jxSRU1iBp6ueYQlSmcxe3TIgGncXHXuOFrM+4+xAed4mbW3Rtb0iZIavFbuhTyRRgZ7klVafdeyn1F887BUdIMUyhDAjOZqE2bX1cTdVynl18d4L5rk32HqoEKMKkYEe0jxQiHPcWl9cYdneXA7lN6+kfWP9Vl9MYUSnttILzI4FerlyYx9tJv6D1Zmmx3OLK1mRfgKAyX0lXa41c9HreGS0Nkv0/soMiipqANiUJelyQliCBETCIu4eGkuApwsZJ8v4tr454IXIAlEhLiwu2JuhCVoJ7s//yLLYflRVZWH9+qHxDt6M1RFMHxKLToHV+06aZti/3XKEOqNKn+gA4ttKOlVrd1X3cBJCvCmurGXO6kxUVTV9X/aR9hRCmJUERMIifNxdeOAy7erW60v3UVH950o5DTbVpwD0lYBIiPO6baBWXOGrzYfPWk9gTnuOFnPoVDnuLjqGSyNPuxcZ6Gla5zVndSZGo2rqPTS5r5TadgR6ncJjYzoC8PG6g2w/XMjx4koMOoWekRIQCWFOEhAJi7m5fxTh/h7kFlfxyfqD531OVW0d2w8XAtJxW4gLGZ7Qlug2nhRX1vKDhUpwNxRTGJ7QFi83g0X2IcyroVHrTzuO8t3WIxw+VYGPm0H6RzmQMYkhdI/wo7y6jofnbwega7gfHq6S0iqEOUlAJCzGzaDn8bEJALy3MoPC8uo/PWfXkSKqa4208XIlNsjL2kMUolXQSnDHAJYpwa2qKr/t0tYPXS4n061GUoQ//WMDqTWq/P2H3QBM7BmGp6sEtI5CURSeGKs1PT90SltDKO0phDA/CYiERU3sHk7nUF9KKmt5d2XGn36/uX6BaJ+YAOm4LcRFXN8nAk9XPftyS9mQYd4S3PtyS8nMK8NVr+OyTm3Num1hWfcMiwOgur54zeRkSZdzNIPi2zAgto3pfp9oSZcTwtwkIBIWpdMp/HWclgM9d30WOYUVZ/1e+g8J0Ti+7i5c28syJbgbZoeGJgTh4+5i1m0LyxqeEEzHEK2AQtdwX7qG+9l4RMLcFEXh8bHa96hOgT7yfSmE2UlAJCxueEIw/WMDqa418saSfabHjUbV1FNBUgCEuLRp9cUVlqblcviU+Upwm5qxSnW5VkdRFP5+RWfC/T14bHRHWw9HWEjv6ADeuLEH/53Si0AvV1sPRwiHIwGRsDhFUXjq8s4AfLf1COnHSwCt43ZxfcftxFBfWw5RiFYhvq0Pg+ODMKrwxR/ZZtnmgROlpOeWYNApjOocYpZtCusamhDMuqcuY4SkOzq0ST3DuSJJLloIYQkSEAmr6BHpz/hu7TCqWrNWOJ0uJx23hWi82+obtc7ffPii5ewba1F9M9ZB8UH4eUq6nBBCCOcjZ6HCah4f0xG9TmFp2gk2Z506XVAhWtLlhGisEZ3aEhnoQVFFDT9ub3kJ7t8amrFKdTkhhBBOSgIiYTWxwd7cmBwJwMyFe00dt5PbS8UcIRpLr1OYZqYS3Nn5ZaQeK0avUxidKAGREEII5yQBkbCqv4zsgIeLni3ZBRwvrsRFLx23hWiq6/tE4uGiZ+/xEjbWX1hojoX1xRT6xwbKQm0hhBBOSwIiYVVtfd25c3B7033puC1E0/l5uHB1r3AA5q7LavZ2FjY0Y5XqckIIIZyYBETC6u4eFktA/eLtvtJPQYhmaSiu8Hvq8T/192qMIwXl7DhShKLA2C6SLieEEMJ5SUAkrM7X3YWXrulG90h/JveVrupCNEdCiA8D49pgVOHzDU0vwd3Qe6hvTCDBPm7mHp4QQgjRakhAJGxiXNdQFtw/iPZBXrYeihCt1ukS3IeorGlaCe6FpmasMjskhBDCuUlAJIQQrdTIziFEBHhQWF7DT9uPNvp1x4sq2ZKtlb0fJ+uHhBBCODkJiIQQopXS6xRu6R8NwCdNKMHd0Iy1d3QA7fzcLTY+IYQQojWQgEgIIVqxG5MjcXfRkXas2NTs+FIkXU4IIYQ4TQIiIYRoxfw9Xbm6Z30J7vUHL/n8kyVVbMrSeheNk4BICCGEkIBICCFau2n1xRUW78nl6CVKcC/ecxxVhe4RfkQEeFphdEIIIYR9k4BICCFauU7tfOkfG0idUeXLjRcvwb2wfv3Q5d2kmIIQQggBEhAJIYRDaCjBPW/T4QuW4D5VVs0fmVq6nKwfEkIIITQSEAkhhAMY1TmEcH8PTpVV8/OO85fgXpJ6nDqjSmKoL9FtpAeYEEIIATYMiPLz82nfvj1ZWVmmx3bv3k1ycjIBAQE88cQTjS4hK4QQzs6g13FzfQnuuRcowf3bLq263PhuMjskhBBCNLBJQJSXl8eECRPOCoaqqqq48sor6d27NykpKaSmpjJ37lxbDE8IIVqlycmRuBl07DlabGq82qCovIZ1B/IAWT8khBBCnMkmAdHkyZOZPHnyWY8tXLiQoqIiXnvtNeLi4njxxRf56KOPbDE8IYRolQK8XJnUo6EEd9ZZv1ualkutUaVjiA9xwd42GJ0QQghhn2wSEM2ePZuHH374rMd27NhB//798fTUysAmJSWRmpp6wW1UVVVRXFx81o8QQji7hhLcC3cf53hRpenxhupy0ntICCGEOJtNAqLY2Ng/PVZcXEz79u1N9xVFQa/XU1Bw/s7rL730En5+fqafyMhIi41XCCFai8QwX/q2P7sEd0llDav3aely4yVdTgghhDiL3VSZMxgMuLm5nfWYu7s75eXl533+008/TVFRkenn8OHD1himEELYvYYS3P/beIjKmjqW7z1BdZ2R2GAvEkIkXU4IIYQ4k8HWA2gQGBjI7t27z3qspKQEV1fX8z7fzc3tTwGUEEIIGJMYQqifO8eKKvl15zGWpOYCML5rKIqi2Hh0QgghhH2xmxmi5ORk/vjjD9P9rKwsqqqqCAwMtOGohBCi9TmzBPecNZms3HcCkPVDQgghxPnYTUA0dOhQioqK+OyzzwCYOXMmo0aNQq/X23hkQgjR+kzpG4WrQcfe4yVU1hiJCvSkS5ivrYclhBBC2B27SZkzGAzMnj2bm266iSeeeIK6ujpWrVpl62EJIUSrFOjlysTuYXyz5QgAl3drJ+lyQgghxHnYNCA6t5P6pEmT2L9/PykpKQwcOJDg4GAbjUwIIVq/aQNjTAHR+K5SXU4IIYQ4H7uZIWoQHh5OeHi4rYchhBCtXtdwP54Y25Hy6lqSIvxsPRwhhBDCLtldQCSEEMJ87h8Rb+shCCGEEHbNbooqCCGEEEIIIYS1SUAkhBBCCCGEcFoSEAkhhBBCCCGclgREQgghhBBCCKclAZEQQgghhBDCaUlAJIQQQgghhHBaEhAJIYQQQgghnJYEREIIIYQQQginJQGREEIIIYQQwmlJQCSEEEIIIYRwWhIQCSGEEEIIIZyWBERCCCGEEEIIpyUBkRBCCCGEEMJpSUAkhBBCCCGEcFoSEAkhhBBCCCGclgREQgghhBBCCKclAZEQQgghhBDCaUlAJIQQQgghhHBaBlsPwFxUVQWguLjYxiMRQgghhBBC2FJDTNAQI1yMwwREJSUlAERGRtp4JEIIIYQQQgh7UFJSgp+f30Wfo6iNCZtaAaPRyNGjR/Hx8UFRFFsPh+LiYiIjIzl8+DC+vr62Ho6wAHmPHZ+8x85B3mfHJ++xc5D32fE15T1WVZWSkhLCwsLQ6S6+SshhZoh0Oh0RERG2Hsaf+Pr6ykHp4OQ9dnzyHjsHeZ8dn7zHzkHeZ8fX2Pf4UjNDDaSoghBCCCGEEMJpSUAkhBBCCCGEcFoSEFmIm5sbzz33HG5ubrYeirAQeY8dn7zHzkHeZ8cn77FzkPfZ8VnqPXaYogpCCCGEEEII0VQyQySEEEIIIYRwWhIQCSGEEEIIIZyWBERCCCGEEEIIpyUBkRDN8OCDD6IoiuknPj7e1kMSQjRSfn4+7du3Jysry/SYHNNCtC4LFiwgNjYWg8FAv379SEtLA+RYdkT5+fmsX7+evLw8i+1DAiIz2717N8nJyQQEBPDEE08gNSsc05YtW/j1118pKCigoKCAbdu22XpIwkzOd7Isx7XjyMvLY8KECWe9vyDHtKO50MmyHMuOISMjg9tvv52ZM2eSk5NDdHQ006dPB+RYdjTz588nPj6e+++/n6ioKObPnw+Y/1iWgMiMqqqquPLKK+nduzcpKSmkpqYyd+5cWw9LmFltbS27d+9m6NCh+Pv74+/vj4+Pj62HJczgfCfLclw7lsmTJzN58uSzHpNj2rFc6GRZjmXHkZaWxosvvsgNN9xASEgI9957LykpKXIsO5jCwkIefPBB1qxZw7Zt2/jggw948sknLXMsq8JsfvjhBzUgIEAtKytTVVVVt2/frg4aNMjGoxLmtmXLFtXb21uNi4tT3d3d1bFjx6rZ2dm2HpYwg5EjR6pvvPGGCqgHDx5UVVWOa0eTkZGhqqp61nssx7Rj+fnnn9X33nvPdH/58uWqq6urHMsO7L333lMTExPlWHYwhw4dUr/44gvT/R07dqg+Pj4WOZZlhsiMduzYQf/+/fH09AQgKSmJ1NRUG49KmFtaWhpdunRh3rx5pKam4uLiwj333GPrYQkzmD17Ng8//PBZj8lx7VhiY2P/9Jgc045lwoQJzJgxw3Q/PT2d+Ph4OZYdVHV1NbNmzeK+++6TY9nBREZGMnXqVABqamqYNWsW11xzjUWOZUOLRytMiouLad++vem+oijo9XoKCgoICAiw4ciEOU2dOtV0gAK8/fbbxMbGUlxcjK+vrw1HJlrqfCfLclw7PjmmHVfDyfIjjzxCZmamHMsO6JlnnsHb25u7774bFxcXOZYd0I4dOxgxYgSurq7s3buXF154wezHsswQmZHBYMDNze2sx9zd3SkvL7fRiIQ1+Pv7YzQaOXbsmK2HIixAjmvnI8e04zjzZFmOZcezZMkS3n//ff73v//h4uLyp9/LsewYkpKSWLZsGV26dOH222+3yLEsAZEZBQYGcvLkybMeKykpwdXV1UYjEpbw6KOP8vXXX5vub968GZ1OR2RkpA1HJSxFjmvHJ8e0Yzr3ZFmOZceSmZnJ1KlTee+990hMTATkWHZUiqLQs2dP5s6dy4IFCyxyLEvKnBklJyfz4Ycfmu5nZWVRVVVFYGCgDUclzK1Hjx78/e9/p127dtTW1vLggw9y2223mXJZhWOR49rxyTHteM53sizHsuOoqKhgwoQJTJo0iYkTJ1JaWgpA9+7d5Vh2IMuXL2fhwoW8+uqrgJaxAdCpUyfzH8stKskgzlJTU6MGBwern376qaqqqnrPPfeoEyZMsPGohCU89dRTqr+/vxoZGak+9NBDamlpqa2HJMyIMyqQyXHtmM58j1VVjmlHUl5ernbu3Fm966671JKSEtNPdXW1HMsO4ocfflCBP/0cPHhQjmUHkpOTo/r4+KgffPCBeujQIfXWW29Vx44da5HvZUVVpSuZOf3444/cdNNN+Pj4UFdXx6pVq+jSpYuthyWEaAJFUTh48CAxMTGAHNdCtCY//vgjV1999Z8eP3jwINu3b5djWYhWZPHixTzyyCMcOXKEsWPH8u677xIcHGz272UJiCwgJyeHlJQUBg4cSHBwsK2HI4QwAzmuhXAMciwL4RjMeSxLQCSEEEIIIYRwWlJlTgghhBBCCOG0JCASQgghhBBCOC0JiIQQQgghhBBOSwIiIYQQQgghhNOSgEgIIYQQQgjhtCQgEkIIJ/TPf/4TLy8vCgsLAa3Tt6IoZGVltWibt912m1nGZy47d+6kZ8+euLu706FDB2pqai76/Ntuuw1FUVAUBT8/P8aPH09GRoaVRgtz585l+PDhVtufEEIICYiEEMJplZeX8/HHH9t6GBb1/PPPM2DAALKzs/niiy/Q6/WXfM2MGTMoKChg48aNuLi4cPvtt1thpEIIIWxFAiIhhHBSer2ed999F0duR5efn0/fvn0JCQmhX79+6HSX/tpzc3PD39+fTp068cQTT7B161YrjFQIIYStSEAkhBBOavjw4Zw8eZKFCxee9fi5aVsN6XQAMTExzJgxg3bt2vHkk08yceJEgoOD2bJlCwCnTp1i+PDh+Pr6MnXqVMrKykzbWbRoEd26dcPf35/p06dTVVVl+l1MTAxLly7lb3/7G+3atWPHjh2N+jt8++23dOzYkaCgIB544AEqKysBbZZHURRWrVrF7bffjqIozJgxo0n/PjU1NXz//ffExsZecn/npguuXLmSmJiYs373r3/9C39/f6Kjo1mzZo3puS+88ALBwcHEx8f/KfiaNWsWoaGh+Pr6MmXKlLP+zYQQQpiHBERCCOGkvL29ueOOO3j77beb9LqioiKeffZZXnnlFW677TYSExNZtGgRAD///DPTpk0jJSWF9PR0nn/+eQAyMjKYOHEijzzyCFu2bGHLli28+uqrZ2332Wef5ejRo8ybN4+4uLhLjiMlJYVp06bx8ssvs3btWlJSUnjqqacAeP311ykoKGDQoEG88847FBQU8Prrrzfq7/fee+/h7++Pl5cXv/76Kx999NEl93cpv/32GwcOHGDr1q0MGjSIv//97wD89NNPvP7663z33Xd89tlnfPnll6bX7N27l6eeeoqvvvqKrVu3cuDAAebOnduo/QkhhGg8CYiEEMKJPfDAAyxZsoQDBw40+jXTpk2jS5cuhISEcPXVV9O+fXtTsYJ+/fpx++23k5CQwFNPPcW3334LwLx58+jZsyd33HEHcXFxzJgxg59++ums7fr5+TF37lxGjBiBt7f3JccxZ84cpk6dyqRJk+jUqROvvfYas2fPRlVVPDw88Pf3x2Aw4Onpib+/Px4eHo36+02dOpXt27eTkJDAHXfcQXJy8iX3dyl6vZ7Zs2cTGxvLbbfdxuHDhwH44YcfmDp1KkOHDmXgwIFMnz7d9Bp3d3cAqqqqiIqKYuPGjdx1112N+jsIIYRoPAmIhBDCicXFxTFu3DjefffdCz6nvLz8rPsNJ+oNf56pffv2pttRUVEcO3YMgJycHLZu3Yq/vz/+/v489thjHDp06KzXPvjgg00a++HDh89KZ4uNjaWiooK8vLwmbedcvr6+ptTAOXPmmAKepuzv3H+zAQMGmP69XF1dTds8duwYkZGRZ22zQUxMDB9++CFPPfUUbdq04cYbb2zx300IIcSfSUAkhBBO7uGHHz5rtkZRFOrq6kz3U1JSGr2thpkP0IKgkJAQACIiIrjqqqvYvn0727dvZ8eOHSxZsuSs13p5eTVp3FFRUWRmZpruZ2Rk4OnpSVBQUJO2cyG33norx48fZ9myZZfc36X+zXx9fc+7j7Zt23L06FHT/TODxCNHjpCUlMSWLVs4dOgQBQUFvPDCC2b5uwkhhDhNAiIhhHByo0aNomPHjqb7ERER7Nmzh4KCAnJzc5k1a1ajt7V+/Xq++OILDhw4wKuvvsqkSZMAmDJlCmvWrGH//v0AvPnmmy0uZz19+nS+/PJLfvzxR9LT03nssce4++67TQUgWsrX15ebbrqJ2bNnX3J/ERERbN68maqqKvbt28ecOXMatY+JEyfy5Zdfsn79ejZu3HjW61JTU7n88stZt24dZWVlKIqC0Wg0y99NCCHEaRIQCSGE4KGHHjLdHjFiBOPGjaNbt25ceeWV/Pvf/270dsaOHcucOXPo3bs3UVFR/Otf/wK0VLBPP/2URx99lC5durB7927mzZvXojH36dOHTz/9lCeffJJBgwbRu3dvXnrppRZt81z33nsvP/74IydOnLjo/iZPnkxUVBQJCQncddddjR7HNddcw3333cfEiROZNm0aEydONP1uzJgx3HPPPVx//fUkJCSgqqqpGIMQQgjzUVRHbkAhhBBCCCGEEBchM0RCCCGEEEIIpyUBkRBCCCGEEMJpSUAkhBBCCCGEcFoSEAkhhBBCCCGclgREQgghhBBCCKclAZEQQgghhBDCaUlAJIQQQgghhHBaEhAJIYQQQgghnJYEREIIIYQQQginJQGREEIIIYQQwmn9P1FK95dj4T2/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(err_train, err_val, title):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(err_train, label='Training Error')\n",
    "    plt.plot(err_val, label='Validation Error')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Number of Rounds')\n",
    "    plt.ylabel('Mean Squared Error')\n",
    "    plt.legend()\n",
    "    \n",
    "plot_learning_curve(err_train_leave_p_out, err_val_leave_p_out, 'Leave-p-out Learning Curve')\n",
    "plt.savefig('Leave-p-out.png', dpi=300) # Make figure clearer\n",
    "plot_learning_curve(err_train_k_fold, err_val_k_fold, 'K-fold Learning Curve')\n",
    "plt.savefig('K-fold.png', dpi=300) # Make figure clearer\n",
    "plot_learning_curve(err_train_bootstrapping, err_val_bootstrapping, 'Bootstrapping Learning Curve')\n",
    "plt.savefig('Bootstrapping.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf3d019-c117-413e-ac09-45fe2eab2998",
   "metadata": {},
   "source": [
    "### 步骤5 使用早停法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4f1bf893-1754-46f0-ba58-635191908a94",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_poly_mdl(x_tr, y_tr, x_te, y_te, lr=100, max_epoch=1000000, batch_size=1, p=0, disp=False, stop=True):\n",
    "    '''\n",
    "    Learning weight w and bias b\n",
    "    p is the order of polynomial\n",
    "    With early stopping.\n",
    "    '''\n",
    "    \n",
    "    # Train logs\n",
    "    logs = {}\n",
    "    logs['epoch_itr'] = []\n",
    "    logs['loss_train'] = []\n",
    "    logs['loss_test'] = []\n",
    "    logs['err_train'] = []\n",
    "    logs['err_test'] = []\n",
    "\n",
    "    # Initialize w, b, and exponential x\n",
    "    b = 0.0\n",
    "    if p > 0:\n",
    "        w = np.random.randn(p)\n",
    "        x_tr = np.array([x_tr ** (i+1) for i in range(p)]).T\n",
    "        x_te = np.array([x_te ** (i+1) for i in range(p)]).T\n",
    "    else:\n",
    "        w = np.random.randn(x_tr.shape[1])\n",
    "\n",
    "    # For early stop\n",
    "    min_err = float('inf')\n",
    "    err_no_improvement = 0\n",
    "    early_stop = 100\n",
    "\n",
    "    # Model training process\n",
    "    for ie in range(max_epoch):\n",
    "        idx = np.random.permutation(x_tr.shape[0])  # Shuffle x\n",
    "        dw = np.zeros(w.size)\n",
    "        db = 0.0\n",
    "\n",
    "        for ib in range(0, x_tr.shape[0], batch_size):\n",
    "            batch_idx = idx[ib:ib+batch_size]\n",
    "            x_batch = x_tr[batch_idx, :]\n",
    "            y_batch = y_tr[batch_idx]\n",
    "\n",
    "            y_batch_pred = np.matmul(x_batch, w) + b\n",
    "            y_batch_diff = y_batch_pred - y_batch\n",
    "\n",
    "            # Compute partial derivative of w and b\n",
    "            for i in range(w.size):\n",
    "                dw[i] += np.mean(y_batch_diff * x_batch[:, i])\n",
    "            db += np.mean(y_batch_diff)\n",
    "\n",
    "        # Update w and b\n",
    "        w -= lr * dw / len(x_tr)\n",
    "        b -= lr * db / len(x_tr)\n",
    "\n",
    "        # Compute epoch loss and err\n",
    "        y_tr_pred = np.matmul(x_tr, w) + b\n",
    "        y_tr_diff = y_tr_pred - y_tr\n",
    "        loss_train = np.mean(y_tr_diff ** 2) / 2\n",
    "        err_train = np.mean(np.abs(y_tr_diff))\n",
    "        logs['loss_train'].append(loss_train)\n",
    "        logs['err_train'].append(err_train)\n",
    "\n",
    "        y_te_pred = np.matmul(x_te, w) + b\n",
    "        y_te_diff = y_te_pred - y_te\n",
    "        loss_test = np.mean(y_te_diff ** 2) / 2\n",
    "        err_test = np.mean(np.abs(y_te_diff))\n",
    "        logs['loss_test'].append(loss_test)\n",
    "        logs['err_test'].append(err_test)\n",
    "\n",
    "        # Display training process\n",
    "        if (ie + 1) % 10000 == 0 and disp:\n",
    "            print(f'Epoch {ie+1}: train loss ({loss_train:.8f}), test loss ({loss_test:.8f}), train error ({err_train:.4f}), test error ({err_test:.4f})')\n",
    "\n",
    "        # Early stop\n",
    "        if err_test < min_err:\n",
    "            min_err = err_test\n",
    "            err_no_improvement = 0\n",
    "        else:\n",
    "            err_no_improvement += 1\n",
    "\n",
    "        if err_no_improvement >= early_stop:\n",
    "            print(f'Early stopping at epoch {ie+1}')\n",
    "            break\n",
    "\n",
    "    # Change logs to array\n",
    "    logs['loss_train'] = np.array(logs['loss_train'])\n",
    "    logs['err_train'] = np.array(logs['err_train'])\n",
    "    logs['loss_test'] = np.array(logs['loss_test'])\n",
    "    logs['err_test'] = np.array(logs['err_test'])\n",
    "\n",
    "    return w, b, logs"
   ]
  }
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